Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları

Mert Ökten

doi:10.51764/smutgd.1542508

Review

Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları

Year 2024, Volume: 7 Issue: 2, 153 - 171, 14.12.2024

Mert Ökten

https://doi.org/10.51764/smutgd.1542508

Abstract

Optimizasyon, tüm olası alternatifler arasından bir problemin en optimal çözümünü belirleme sürecidir. Enerji sistemlerinde metasezgisel optimizasyon algoritmaları, karmaşık enerji problemlerini çözmede önemli bir rol oynamaktadır. Metasezgisel optimizasyon algoritmaları, genetik algoritmalar, parçacık sürü optimizasyonu, simüle edilen tavlama, karınca kolonisi optimizasyonu gibi doğal süreçlerden esinlenerek geliştirilen ve genellikle bilgisayar tabanlı modellerle kullanılan özel optimizasyon yöntemleridir. Metasezgisel optimizasyon algoritmaları, büyük veri setleriyle çalışabilir ve farklı kısıtlamalar altında optimize edilmesi gereken çok sayıda değişkeni ele alabilirler. Bu nedenle enerji sektöründe sürdürülebilirlik, verimlilik ve karlılık açısından büyük öneme sahiptirler. Bu algoritmalar, enerji verimliliğini artırmak, enerji maliyetini azaltmak, enerji üretimi, dağıtımı, tüketimi ve depolanması gibi enerji sistemlerinin farklı bileşenlerini optimize etmek için, yenilenebilir enerji kaynaklarını entegre etmek ve enerji sistemlerinin karbon ayak izini azaltmak gibi çeşitli hedeflere ulaşmak için kullanılmaktadırlar. Bu çalışmada, enerji sistemleri uygulamalarında metasezgisel optimizasyon algoritmalarının kullanımı örnekler üzerinden incelenmiştir. İncelenen 2532 makale dikkate alındığında en çok genetik algoritma (%37.4) ile parçacık sürü optimizasyonunun (%25.5) kullanıldığı görülmüştür. Bu algoritmaların kullanımı ile karmaşık problemlerin çözümlerinin daha kolaya indirgendiği görülmüştür.

Keywords

Enerji sistemleri, Enerji verimliliği, Metasezgisel optimizasyon algoritmaları, Sürdürülebilirlik

References

Abbass, H.A. (2001). “MBO: Marriage in honey bees optimisation: A haplometrosis polygynous swarming approach”, In Proceedings of the Congress on Evolutionary Computation—CEC, 27–30 May, (pp. 207–214). Seoul, Korea.
Abdechiri, M., Meybodi, M.R., & Bahrami, H. (2013). “Gases brownian motion optimization: An algorithm for optimization (GBMO).” Applied Software Computational, 13, 2932–2946.
Abdollahzadeh, B., Gharehchopogh, F.S., Khodadadi, N., & Mirjalili, S. (2022). “Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems.” Advances in Engineering Software, 174, 103282.
Abualigah, L., Hanandeh, E.S., Zitar, R.A., Thanh, C.L., Khatir S., & Gandomi, A.H. (2023). “Revolutionizing sustainable supply chain management: A review of metaheuristics.” Engineering Applications of Artificial Intelligence, 126(A), 106839.
Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-Qaness, M.A.A., & Gandomi, A.H. (2021). “Aquila optimizer: a novel meta-heuristic optimization algorithm.” Computational Industry Engineering, 157, 107250.
Aghabegloo, M., Rezaie, K., Torabi, S.A., & Yazdani, M. (2023). “A metaheuristic-driven physical asset risk management framework for manufacturing system considering continuity measures.” Engineering Applications of Artificial Intelligence, 126(A), 106789.
Ahrari, A., & Atai, A.A. (2010). “Grenade explosion method: A novel tool for optimization of multimodal functions.” Applications Software Computational, 10, 1132–1140.
Ala, A., Mahmoudi, A., Mirjalili, S., Simic, V., & Pamucar, D. (2023). “Evaluating the performance of various algorithms for wind energy optimization: A hybrid decision-making model.” Expert Systems with Applications, 221, 119731.
Alsattar, H.A., Zaidan, A.A., & Zaidan, B.B. (2020). “Novel meta-heuristic bald eagle search optimisation algorithm.” Artificial Intelligent Revolution, 53, 2237–2264.
Altay, O. (2022a). “Güncel metasezgisel yöntemlerin standart kalite testi fonksiyonlarında karşılaştırılması.” International Journal Pure Applied Science, 8(2), 286-301.
Altay, O. (2022b). “Chaotic slime mould optimization algorithm for global optimization.” Artificial Intelligence Review, 55, 3979-4040.
Altay, O. & Varol Altay, E. (2022). “Investigation of slime mould algorithm and hybrid slime mould algorithms performance in global optimization problems.” Dicle University Journal of Engineering, 13(4), 661-671.
Altay, O. & Varol Altay, E. (2023). “A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection.” PeerJ Computational Science, 9, 1526.
Ashrafi, S.M., & Dariane, A.B. (2013). “Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody search (MS).” Engineering Applied Artificial Intelligent, 26, 1301–1321.
Askarzadeh, A. (2016). “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm.” Computers & Structures, 169, 1–12.
Atashpaz-Gargari, E., & Lucas, C. (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 25–28 September, (pp. 4661–4667). Singapore.
Azizi, M., Aickelin, U., Khorshidi, H.A., & Shishehgarkhaneh, M.B. (2023). “Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization.” Scientific Reports, 13, 226.
Baykasoglu, A., & Senol, M.E. (2016). “Combinatorial optimization via weighted superposition attraction.” In Proceedings of the International Conference on Operations Research of the German Operation Socienty (GOR 2016), 30 August–12 September, Hamburg, Germany.
Bayraktar, Z., Komurcu, M., & Werner, U.H. (2010). “Wind driven optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In Proceedings of the 2010 IEEE Antennas and Propagation Society International Symposium, 11–17 July, (pp. 1–4). Toronto, ON, Canada.
Bhattacharya, A., & Chattopadhyay, P. (2011). “Application of biogeography-based optimisation to solve different optimal power ﬂow problems.” IET Generation Transmission Distribute, 5, 70–80.
Binetti, G., Davoudi, A., Naso, D., Turchiano, B., & Lewis, F.L. (2013). “A distributed auction-based algorithm for the nonconvex economic dispatch problem.” IEEE Transfer Industry Information, 10, 1124–1132.
Braik, M., Sheta, A., & Al-Hiary, H. (2021). “A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm.” Neural Computational Applied, 33, 2515–2547.
Boettcher, S., & Percus, A.G. (2001). “Optimization with extremal dynamics.” Physics Revolution Letter, 86, 5211–5214.
Chen, H., Wang, M., & Zhao, X. (2020). “A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems.” Applied Mathematical Computational, 369, 124872.
Cheng, M.Y., & Prayogo, D. (2014). “Symbiotic organisms search: A new metaheuristic optimization algorithm.” Computers & Structures, 139, 98–112.
Cheraghi, R., & Jahangir, R.H. (2023). “Multi-objective optimization of a hybrid renewable energy system supplying a residential building using NSGA-II and MOPSO algorithms.” Energy Conversion and Management, 294, 117515.
Chicco, G., & Mazza, A. (2019). “Heuristic optimization of electrical energy systems: Reﬁned metrics to compare the solutions.” Sustainable Energy Grids Network, 17, 100197.
Chou, J.S., & Truong, D.N. (2021). “A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean.” Applied Mathematical Computational, 389, 125535.
Chou, J.S., Nguyen, N.M., & Chang, C.P. (2022). “Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning.” Applied Soft Computing, 130, 109642.
Chu, S.C., Tsai, P.W., & Pan, J.S. (2006). “Cat swarm optimization.” In Trends in Artiﬁcial Intelligence (PRICAI 2006), Q. Yang, & G. Webb, (Eds.), Springer, (pp. 854–858).
Civicioglu, P. (2012). “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.” Computational Geoscience, 46, 229–247.
Civicioglu, P. (2013a). “Backtracking search optimization algorithm for numerical optimization problems.” Applied Mathematical Computational, 219, 8121–8144.
Civicioglu, P. (2013b). “Artiﬁcial cooperative search algorithm for numerical optimization problems.” Informatics Science, 229, 58–76.
Cuevas, E., Oliva, D., Zaldivar, D., Perez, M.A., Sossa-Azuela, H., & Zaldívar, D. (2012). “Circle detection using electro-magnetism optimization.” Informatics Science, 182, 40–55.
Dai, C., Chen, W., & Zhu, Y. (2006). “Seeker Optimization Algorithm.” In Computational Intelligence and Security (CIS 2006), Y. Wang, Y. Cheung & H. Liu (Eds.), Springer, (pp. 225–229).
Damgacı, E., Boran, K. & Boran, F.E. (2017). “Sezgisel bulanık TOPSIS yöntemi kullanarak Türkiye’nin yenilenebilir enerji kaynaklarının değerlendirilmesi.” Politeknik Dergisi, 20(3), 629-637.
De Castro, L., Von Zuben, C.J., & De Castro, L.N. (2002). “Learning and optimization using the clonal selection principle.” IEEE Transmission Evolution Computer, 6, 239–251.
Değer, K., Özkaya, M.G., & Boran, F.E. (2023). “Modelling and analysis of future energy scenarios on the sustainability axis.” Journal of Polytechnic, 26(2), 665-678.
Demir, F.B., Tuncer, T., & Kocamaz, A.F. (2020). “A chaotic optimization method based on logistic-sine map for numerical function optimization.” Neural Computing and Applications, 32(17), 14227–14239.
Detwal, P.K., Agrawal, R., Samadhiya, A., & Kumar, A. (2023). “Metaheuristics in circular supply chain intelligent systems: A review of applications journey and forging a path to the future.” Engineering Applications of Artificial Intelligence, 126(D), 107102.
Dhiman, G., & Kumar, D. (2019). “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems.” Knowledge-Based System, 165, 169–196.
Dhiman, G., Garg, M., Nagar, M., Kumar, V., & Dehghani, M. (2021). “A novel algorithm for global optimization: Rat swarm optimizer.” Journal of Ambient Intelligence Humanization Computational, 12, 8457–8482.
Doering, J., Kizys, R., Juan, A.A., Fito, A., & Polat, O. (2019). “Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends.” Operations Research Perspectives, 6, 100121.
Dogan, B., & Olmez, T. (2015). “A new metaheuristic for numerical function optimization: Vortex search algorithm.” Informatics Science, 293, 125–145.
Dokeraglu, T., Deniz, A., & Kiziloz, H.E. (2022). “A comprehensive survey on recent metaheuristics for feature selection.” Neurocomputing, 494, 269-296.
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). “Positive feedback as a search strategy. Politecnico di Milano: Dipartimento di Elettronica.” Technical Report, 91, 16.
Duan, H., & Qiao, P. “Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning.” International Journal of Intelligence Computer Cybernetics, 7, 24–37.
El-Abd, M. (2013). “An improved global-best harmony search algorithm.” Applied Mathematical Computational, 222, 94–106.
Emami, H., & Derakhshan, F. (2015). “Election algorithm: A new socio-politically inspired strategy.” AI Community, 28, 591–603.
Erlich, I., Venayagamoorthy, G.K., & Worawat, N. (2010) “A mean-variance optimization algorithm.” In Proceedings of the 2010 IEEE World Congress on Computational Intelligence, 18–23 July, Barcelona, Spain.
Eskandar, H., Sadollah, A., Bahreininejad, A. & Hamdi, M. (2012). “Water cycle algorithm: A novel metaheuristic optimization method for solving constrained engineering optimization problems.” Computers & Structures, 110, 151–166.
Eusuff, M.M., & Lansey, K.E. (2003). “Optimization of water distribution network design using the shuffled frog leaping algorithm.” Journal of Water Resource Planning Management, 129, 210–225.
Eusuff, M., Lansey, K.E., & Pasha, F. (2006). “Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization.” Engineering Optimization, 38, 129–154.
Fanian, F., & Rafsanjani, M.K. (2023). “CFMCRS: Calibration fuzzy-metaheuristic clustering routing scheme simultaneous in on-demand WRSNs for sustainable smart city.” Expert Systems with Applications, 211, 118619.
Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020a). “Equilibrium optimizer: A novel optimization algorithm.” Knowledge-Based Systems, 191, 105190.
Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A.H. (2020b). “Marine predators algorithm: A nature-inspired metaheuristic.” Expert Systems Applied, 152, 113377.
Farmer, J., Packard, N.H., & Perelson, A.S. (1986). “The immune system, adaptation, and machine learning.” Physical Differential Nonlinear Phenomena, 22, 187–204.
Feng, Z.K., Niu, W.J., & Liu, S. (2021). “Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems.” Applied Soft Computing, 98, 106734.
Feo, T.A., & Resende, M.G. (1989). “A probabilistic heuristic for a computationally difficult set covering problem.” Operational Research Letter, 8, 67–71.
Fogel, D.B. (2009). Artiﬁcial Intelligence through Simulated Evolution. Wiley, New York, USA. Gandomi, A.H., & Alavi, A.H. (2012). “Krill herd: A new bio-inspired optimization algorithm.” Community Nonlinear Science Numerical Simulation, 17, 4831–4845.
Ghalizadeh, H., Goh, M., Fazlollahtabar, H., & Mamashli, Z. (2022). “Modelling uncertainty in sustainable-green integrated reverse logistics network using metaheuristics optimization.” Computers & Industrial Engineering, 163, 107828.
Ghasemi-Marzbali, A. (2020). “A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm.” Software Computation, 24, 13003–13035.
Glover, F. (1977). “Heuristics for integer programming using surrogate constraints.” Decision Science, 8, 156–166.
Glover, F. (1989). “Tabu Search: Part I.” ORSA Journal of Computational, 1, 190–206.
Greensmith, J., Aickelin, U., & Cayzer, S. (2000). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In Haptics: Science, Technology, Applications, 3627, 153–167.
Guang, Q., Feng, L., Lijuan, L., Lu, J.W.Z., Leung, A.Y.T., Lu, V.P., & Mok, K.M. (2010). “A quick group search optimizer and its application to the optimal design of double layer grid shells.” AIP Publishing, 1233, 718.
Hansen, N., Müller, S.D., & Koumoutsakos, P. (2013). “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES).” Evolution Computer, 11, 1–18.
Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W., & Mirjalili, S. (2019). “Henry gas solubility optimization: A novel physics based algorithm.” Futurist General Computational Systems, 101, 646–667.
Hashim, F.A., Hussain, K., Houssein, E.H., Mabrouk, M.S., & Al-Atabany, W. (2021). “Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems.” Applied Intelligence, 51, 1531–1551.
Hashim, F.A., & Hussien, A.G. (2022). “Snake optimizer: A novel metaheuristic optimization algorithm.” Knowledge-Based Systems, 242, 108320.
Hayyolalam, V., & Pourhaji Kazem, A.A. (2020). “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems.” Engineering Applied Artificial Intelligence, 87, 103249.
He, S., Wu, Q., & Saunders, J. (2006). “A novel group search optimizer inspired by animal behavioral ecology.” In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, 16–21 July, Vancouver, BC, Canada.
Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). “Harris hawks optimization: Algorithm and applications.” Futurist General Computational Systems, 97, 849–872.
Higashitani, M., Ishigame, A., & Yasuda, K. (2006). “Particle swarm optimization considering the concept of predator-prey behavior.” In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, 16–21 July, (pp. 434–437). Vancouver, BC, Canada.
Holland, J.H. (1992). Adaptation in Natural and Artiﬁcial Systems, MIT Press, Cambridge, MA, USA.
Hosseini, H.S. (2007). “Shah Problem solving by intelligent water drops.” In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 25–28 September, (pp. 3226–3231). Singapore.
Hosseini, H.S. (2011). “Principal components analysis by the galaxy-based search algorithm: A novel metaheuristic for continuous optimization.” International Journal of Computational Science Engineering, 6, 132–140.
Iqbal, M., Azam, M., Naeem, M., Khwaja, A. S. & Anpalagan, A. (2014) “ Optimization classification, algorithms and tools for renewable energy: A review” Renewable and Sustainable Energy Reviews, 39, 640–654.
Jain, M., Singh, V., & Rani, A. (2019). “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” Swarm Evolution Computational, 44, 148–175.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). “Ions motion algorithm for solving optimization problems.” Applied Software Computational, 32, 72–79.
Kaboli, S.H.A., Selvaraj, J., & Rahim, N. (2017). “Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems.” Journal of Computer Science, 19, 31–42.
Karaboga, D., & Basturk, B.A. (207). “Powerful and efficient algorithm for numerical function optimization: Artiﬁcial bee colony (ABC) algorithm.” Journal of Global Optimization, 39, 459–471.
Karami, H., Sanjari, M.J., & Gharehpetian, G.B. (2014). “Hyper-spherical search (HSS) algorithm: A novel meta-heuristic algorithm to optimize nonlinear functions.” Neural Computational Applied, 25, 1455–1465.
Kashan, A.H. (2014). “League championship algorithm (LCA): An algorithm for global optimization inspired by sport championships.” Applied Software Computational, 16, 171–200.
Kashan, A.H. (2015). “A new metaheuristic for optimization: Optics inspired optimization (OIO).” Computational Operation Research, 55, 99–125.
Kaveh, A., & Talatahari, S. (2010). “A novel heuristic optimization method: Charged system search.” Acta Mechanical, 213, 267–289.
Kaveh, A., & Khayatazad, M. (2012). “A new meta-heuristic method: Ray optimization.” Computers & Structures, 112–113, 283–294.
Kaveh, A., & Farhoudi, N. (2013). “A new optimization method: Dolphin echolocation.” Advance Engineering Software, 59, 53–70.
Kaveh, A., & Mahdavi, V. (2014). “Colliding bodies optimization: A novel meta-heuristic method.” Computers & Structures, 139, 18–27.
Kaveh, A., & Dadras, A. (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” Advance Engineering Software, 110, 69–84.
Khan, A.A., Laghari, A.A., Gadekallu, T.R., Shaikh, Z.A., Javed, A.R., Rashid, M., Estrela, V.V., & Mikhaylov, A. (2022). “A drone-based data management and optimization using metaheuristic algorithms and blockchain smart contracts in a secure fog environment.” Computers and Electrical Engineering, 102, 108234.
Khelili, M.A., Slatnia, S., Kazar, O., Merizig, A., & Mirjalili, S. (2023). “Deep learning and metaheuristics application in internet of things: A literature review.” Microprocessors and Microsystems, 98, 104792.
Kiran, M.S., & Kiran, M.S. (2015). “TSA: Tree-seed algorithm for continuous optimization.” Experimental System Applications, 42, 6686–6698.
Kinost, A., Doerner, K.F., & Rinderle-Ma, S. (2022). “Combining metaheuristics and process mining: Improving cobot placement in a combined cobot assignment and job shop scheduling problem.” Procedia Computer Science, 200, 1836-1845.
Kirkpatrick, S., Gelatt, J.C.D., & Vecchi, M.P. (1986). “Optimization by simulated annealing.” World Scientiﬁc Lecture Notes in Physics, 220, 339–348.
Klar, M., Glatt M., & Aurich, J.C. (2023). “Performance comparison of reinforcement learning and metaheuristics for factory layout planning.” CIRP Journal of Manufacturing Science and Technology, 45, 10-25.
Krishnanand, K., & Ghose, D. (2005). “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics.” In Proceedings of the 2005 IEEE Swarm Intelligence Symposium, 8–10 June, (pp. 84–91). Pasadena, CA, USA.
Kulkarni, A.J., Durugkar, I.P., & Kumar, M. (2013). “Cohort Intelligence: A self-supervised learning behavior.” In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, 13–16 October, (pp. 1396–1400). Manchester, UK.
Kumar, A., Misra, R.K., & Singh, D. (2017). “Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase.” In: Proceedings of the 2017 IEEE Congress Evolution Computer, 1835–1842.
Kumar, N., Singh, N., & Vidyarthi, D.P. (2021). “Artificial lizard search optimization (ALSO): A novel nature-inspired metaheuristic algorithm.” Software Computational, 25, 6179–6201.
Kutlu Onay, F., & Aydemir, S.B. (2022). “Chaotic hunger games search optimization algorithm for global optimization and engineering problems.” Mathematics and Computers in Simulation, 192(10), 514–536.
Lam, A.Y.S., & Li, V.O.K. (2010). “Chemical-reaction-inspired metaheuristic for optimization.” IEEE Transmission Evolution Computer, 14, 381–399.
Lee, K.Y., & Vale, Z.A. (2020). Applications of Modern Heuristic Optimization Methods in Power and Energy Systems. Wiley: Hoboken, NJ, USA.
Lessmann, S., Caserta, M., & Arango, I.M. (2011). “Tuning metaheuristics: A data mining based approach for particle swarm optimization.” Expert Systems with Applications, 38(10), 12826-12838.
Li, M.D., Zhao, H., Weng, X.W., & Han, T. (2016). “A novel nature-inspired algorithm for optimization: Virus colony search.” Advance Engineering Software, 92, 65–88.
Li, T., Wang, C.F., Wang, W.B., & Su, W.L. (2005). “A global optimization bionics algorithm for solving integer programming-plant growth simulation algorithm.” Systems Engineering-Theory Practical, 25, 76–85.
Li, X.D., Wang, J.S., Hao, W.K., Zhang, M., & Wang, M. (2022). “Chaotic arithmetic optimization algorithm.” Applied Intelligence, 52(14), 16718–16757.
Liang, Y.C., & Juarez, J.R.C. (2016). “A novel metaheuristic for continuous optimization problems: Virus optimization algorithm.” Engineering Optimization, 48(1), 73–93.
Luo, J., Chen, H., Zhang, Q., Xu, Y., Huang, H., & Zhao, X. (2018). “An improved grasshopper optimization algorithm with application to financial stress prediction.” Applied Mathematical Modelling, 64, 654–668.
MATLAB. (2018). R2019b. The MathWorks Inc., Natick, Massachusetts, USA.
Mehrabian, A., & Lucas, C. (2006). “A novel numerical optimization algorithm inspired from weed colonization.” Ecological Information, 1, 355–366.
Mendi, F., Başkal, T., Boran. K., & Boran, F.E. (2010). “Optimization of module, shaft diameter and rolling bearing for spur gear through algorithm.” Energy Systems with Applications, 37, 8058-8064.
Meng, A.B., Chen, Y.C., Yin, H., & Chen, S.Z. (2014). “Crisscross optimization algorithm and its application.” Knowledge-Based Systems, 67, 218–229.
Mihaly, N.B., Luca, A.V., Simon Varhelyi, M., & Cristea, V.M. (2023). “Improvement of air flowrate distribution in the nitrification reactor of the waste water treatment plant by effluent quality, energy and greenhouse gas emissions optimization via artificial neural networks models.” Journal of Water Process Engineering, 54, 103935.
Mirjalili, S. (2013). “Dragonﬂy algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems.” Neural Computational Applied, 27, 1053–1073.
Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). “Grey wolf optimizer.” International Journal of Advance Engineering Software, 69, 46–61.
Mirjalili, S. (2015a). “Moth-ﬂame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-Based Systems, 89, 228–249.
Mirjalili, S. (2015b). “The ant lion optimizer.” Advance Engineering Software, 83, 80–98.
Mirjalili, S. (2016). “SCA: A sine cosine algorithm for solving optimization problems.” Knowledge-Based Systems, 96, 120–133.
Mirjalili, S., & Lewis, A. (2016). “The whale optimization algorithm.” Advance Engineering Software, 95, 51–67.
Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., & Mirjalili, S.M. (2017). “Salp swarm algorithm: A bio-inspired optimizer for engineering design problems.” Advance Engineering Software, 114, 163–191.
Mladenovi ´C, N., & Hansen, P. (1997). “Variable neighborhood search.” Computational Operational Research, 24, 1097–1100.
Moein, S., & Logeswaran, R. (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” Informatics Science, 275, 127–144.
Moghdani, R., & Salimifard, K. (2018). “Volleyball premier league algorithm.” Applied Software Computational, 64, 161–185.
Mohamed, A.W., Hadi, A.A., & Mohamed, A.K. (2020). “Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm.” International Journal of Machine Learning and Cybernetics, 11, 1501–1529.
Mohammed, H.M., & Rashid, T.A. (2021). “Chaotic ﬁtness-dependent optimizer for planning and engineering design.” Software Computing, 25(22), 14281–14295.
Monismith, D.R., & Mayﬁeld, B.E. (2008). “Slime mould as a model for numerical optimization.” In Proceedings of the IEEE Swarm Intelligence Symposium, 21–23 September, St. Louis, MO, USA.
Moosavian, N., & Roodsari, B.K. (2014). “Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks.” Swarm Evolution Computational, 17, 14–24.
Moscato, P. (1989). “On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms.” In Caltech Concurrent Computation Program (Report 826), California Institute of Technology, pp. 158–179, Pasadena, CA, USA.
Muthiah-Nakarajan, V., & Noel, M.M. (2016). “Galactic swarm optimization: A new global optimization metaheuristic inspired by galactic motion.” Applied Software Computational, 38, 771–787.
Mühlenbein, H., & Pass, G. (1996). “From recombination of genes to the estimation of distributions I. Binary parameters.” In Computer Vision, 1141, 178–187.
Narayanan, A., & Moore, M. (1996). “Quantum-inspired genetic algorithms.” In Proceedings of the IEEE International Conference on Evolutionary Computation ICEC-96, 20–22 May, (pp. 61–66). Nagoya, Japan.
Naruei, I., & Keynia, F. (2021). “A new optimization method based on coot bird natural life model.” Expert Systems with Applications, 115352.
Nassef, A.M., Abdelkareem, M.A., Maghrabie, H.M., & Baroutaji, A. (2023). “Review of metaheuristic optimization algorithms for power systems problems.” Sustainability, 15, 9434.
Pan, W.T. (2012) “A new fruit fly optimization algorithm: Taking the ﬁnancial distress model as an example.” Knowledge-Based Systems, 26, 69–74.
Passino, K. (2002). “Biomimicry of bacterial foraging for distributed optimization and control.” IEEE Controlling Systems, 22, 52–67.
Pelikan, M., Goldberg, M.E., & Cant-Paz, E. (1999). “BOA: The Bayesian optimization algorithm.” In Proceedings of the Genetic and Evolutionary Computation Conference—GECCO-99, I, 13–17 July, (pp. 525–532). Orlando, FL, USA.
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020a). “An improved moth- flame optimization algorithm with hybrid search phase.” Knowledge-Based Systems, 191, 105277.
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020b). “Improving exploration and exploitation via a hyperbolic gravitational search algorithm.” Knowledge-Based Systems, 193, 105404.
Pereira, J.L.J. (2021). “Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization.” Expert Systems Applied, 170, 114522.
Pierezan, J., & Coelho, L.D.S., (2018). “Coyote optimization algorithm: A new metaheuristic for global optimization problems.” In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), (pp. 1–8). 8–13 July, Rio de Janeiro, Brasil.
Poli, R., Kennedy, J., & Blackwell, T. (2007). “Particle swarm optimization.” Swarm Intelligent, 1, 33–57. Punnathanam, V., & Kotecha, P. (2016). “Yin-yang-pair optimization: A novel light weight optimization algorithm.” Engineering Applied Artificial Intelligence, 54, 62–79.
PYTHON. (1995). Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands.
Qais, M.H., Hasanien, H.M., & Alghuwainem, S. (2020). “Transient search optimization: A new meta-heuristic optimization algorithm.” Applied Intelligence, 50(11), 3926–3941.
Qu, G., Cheng, H., Yao, L., Ma, Z., & Zhu, Z. (2010). “Transmission surplus capacity based power transmission expansion planning.” Electrical Power Systems Research, 80, 19–27.
Rabanal, P., Rodríguez, L., & Rubio, F. (2007). “Using river formation dynamics to design heuristic algorithms in swarm.” Evolutionary, and Memetic Computational, 4618, 163–177.
Radosavljević, J. (2018). Metaheuristic Optimization in Power Engineering. The Institution of Engineering and Technology, British Library Cataloguing in Publication Data, Herts, United Kingdom.
Rahmani, R., & Yusof, R. (2014). “A new simple, fast and efficient algorithm for global optimization over continuous search-space problems.” Applied Mathematical Computational, 248, 287–300.
Rahul, K., & Banyal, R.K. (2022). “Metaheuristics approach to improve data analysis process for the healthcare sector.” Procedia Computer Science, 215, 98-103.
Rahkar Farshi, T. (2021). “Battle royale optimization algorithm.” Neural Computational Applied, 33, 1139–1157.
Rakhshani, H., & Rahati, A. (2017). “Snap-drift cuckoo search: A novel cuckoo search optimization algorithm.” Applied Software Computational, 52, 771–794.
Rao, R.V., Savsani, V.J., & Vakharia, D. (2011). “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems.” Computational Deserve, 43, 303–315.
Rao, R.V. (2016). “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems.” International Journal of Industry Engineering Computational, 7, 19–34.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). “GSA: A gravitational search algorithm.” Informatics Science, 179, 2232–2248.
Ray, T., & Liew, K. (2003). “Society and civilization: An optimization algorithm based on the simulation of social behavior.” IEEE Transfer Evolution Computational, 7, 386–396.
Rechenberg, I. (1971). “Evolutionsstrategie–optimierung technischer systeme nach prinzipien der biologischen evolution (in German).” [Ph.D. Thesis, Technical University of Berlin].
Reynolds, R.G. (1994). “An introduction to cultural algorithms.” In Proceedings of the Third Annual Conference on Evolutionary Programming, 24–26 February, (pp. 131–139). San Diego, CA, USA.
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). “Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems.” Applied Software Computational, 13, 2592–2612.
Sakthivel, S., Pandiyan, S.A., Marikani, S., & Selvi, S.K. (2013). “Application of big-bang big-crunch algorithm for optimal power flow problems.” International Journal of Engineering Science, 2, 41–47.
Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J.A. (2014). “The coral reefs optimization algorithm: A novel metaheuristic for efficiently solving optimization problems.” Science World Journal, 1–15.
Salgotra, R., Singh, U., Singh, G., Mittal, N., & Gandomi, A.H. (2021). “A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems.” Computational Methods Applied Mechanical Engineering, 383, 113916.
Salimi, H. (2015). “Stochastic fractal search: A powerful metaheuristic algorithm.” Knowledge-Based Systems, 75, 1–18.
Saremi, S., Mirjalili, S., & Lewis, A. (2017). “Grasshopper optimisation algorithm: Theory and application.” Advance Engineering Software, 105, 30–47.
Satapathy, S.C., & Naik, A. (2016). “Social group optimization (SGO): A new population evolutionary optimization technique.” Complex Intelligence Systems, 2, 173–203.
Savsani, P., & Savsani, V. (2016). “Passing vehicle search (PVS): A novel metaheuristic algorithm.” Applied Mathematical Modelling, 40, 3951–3978.
Sayed, G.I., Darwish, A., & Hassanien, A.E. (2018). “A new chaotic multi-verse optimization algorithm for solving engineering optimization problems.” Journal of Experimental and Theoretical Artiﬁcial Intelligence, 30(2), 293–317.
Shayanfar, H., & Gharehchopogh, F.S. (2018). “Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems.” Applied Software Computational, 71, 728–746.
Shirke, C., Sabar, N., Chung, E., & Bhaskar, A. (2021). “Metaheristic approach for designing robust traffic signal timings to effectively serve varying traffic demand.” Journal of Intelligent Transportation Systems, 26(3), 343-355.
Storn, R. & Price, K. (1997). “Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces.” Journal of Global Optimization, 11, 341–359.
Sulaiman, M.H., & Mustaffa, Z. (2023). “Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building.” Journal of Building Engineering, 76, 107139.
Sulaiman, M.H., Mustaffa, Z., Saari, M.M., Daniyal, H., & Mirjalili, S. (2023). “Evolutionary mating algorithm.” Neural Computational Applied, 35(1), 487–516.
Tan, Y., & Zhu, Y. (2010). “Fireworks algorithm for optimization.” In Computer Vision, 6145, 355–364.
Tang, R., Fong, S., Yang, X.S., & Deb, S. (2012). “Wolf search algorithm with ephemeral memory.” In Proceedings of the Seventh International Conference on Digital Information Management (ICDIM 2012), 22–24 August, (pp. 165–172). Macau, China.
Tarkhaneh, O., Alipour, N., Chapnevis, A., & Shen, H. (2021). “Golden tortoise beetle optimizer: A novel nature-inspired meta-heuristic algorithm for engineering problems.” Arxiv. https://arxiv.org/pdf/2104.01521.pdf
Topal, A.O., & Altun, O. (2016). “A novel meta-heuristic algorithm: dynamic virtual bats algorithm.” Informatics Science, 354, 222–235.
Uymaz, S.A., Tezel, G., & Yel, E. (2015). “Artiﬁcial algae algorithm (AAA) for nonlinear global optimization.” Applied Software Computational, 31, 153–171.
Varol Altay, E., & Alatas, B. (2020). “Bird swarm algorithms with chaotic mapping.” Artiﬁcial Intelligence Review, 53(2), 1373–1414.
Varol Altay, E. & Alatas, B. (2021). “Differential evolution and sine cosine algorithm based novel hybrid multiobjective approaches for numerical association rule mining.” Information Sciences, 554, 198-221.
Varol Altay, E. & Altay, O. (2021). “Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması.” Dicle University Journal of Engineering, 12(5), 729-741.
Varol Altay, E., Gurgenc, E., Altay, O., & Dikici, A. (2022). “Hybrid artificial neural network based on a metaheuristic optimization algorithm for the prediction of reservoir temperature using hydrogeochemical data of different geothermal areas in Anatolia (Turkey).” Geothermics, 104, 102476.
Varol Altay, E. & Altay, O. (2023a). “Assessment of grey wolf optimizer and its variants on benchmark functions”, International Conference on Computing, Intelligence and Data Analytics (ICCIDA) 2022: Computational Intelligence, Data Analytics and Applications. (pp. 55-66), Springer.
Varol Altay, E. & Altay, O. (2023b) “A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer.” Neural Computing and Applications, 35, 529-556.
Wang, G.G., Deb, S., & Coelho, L.D.S. (2015a). “Elephant herding optimization.” In Proceedings of the 3rd International Symposium on Computational and Business Intelligence, 7-9 December, (pp. 1–5). Bali, Indonesia.
Wang, G.G., Deb, S., & Cui, Z. (2015b). “Monarch butterﬂy optimization.” Neural Computational Applied, 31, 1995–2014.
Wang, Q.Y., Lv, X.L., & Zeman, A. (2023). “Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm.” Applied Thermal Engineering, 234, 121141.
Wang, Z., Chen, L., Wang, B., Huang, L., Wang, K., & Ma, R. (2023). “Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors.” Energy, 282, 128795.
Witten, T.A., & Sander, L.M. (1981) “Diffusion-limited aggregation: A kinetic critical phenomenon.” Physical Revolution Letter, 47, 1400–1403.
Xian, S., Zhang, J., Xiao, Y., & Pang, J. (2017). “A novel fuzzy time series forecasting method based on the improved artiﬁcial ﬁsh swarm optimization algorithm.” Software Computational, 22, 3907–3917.
Yampolskiy, R.V., Ashby, L., & Hassan, L. (2012). “Wisdom of artificial crowds: A metaheuristic algorithm for optimization.” Journal of Intelligence Learning Systems Applications, 4(2), 10.
Yang, L., Gao, S., Yang, H., Cai, Z., Lei, Z., & Todo, Y. (2021). “Adaptive chaotic spherical evolution algorithm.” Memetic Computing, 13(3), 383–411.
Yang, X.S., & Deb, S. (2009). ”Cuckoo search via lévy ﬂights.” In Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 9–11 December, (pp. 210–214). Coimbatore, India.
Yang, X.S., & Deb, S. (2010). “Eagle strategy using lévy walk and fireﬂy algorithms for stochastic optimization.” In Studies in Computational Intelligence, 284, 101–111.
Yang, X.S. (2010a). “Fireﬂy algorithm, stochastic test functions and design optimization.” International Journal Bio-Inspired Computational, 2, 78.
Yang, X.S. (2010b). “A New Metaheuristic Bat-Inspired Algorithm.” In Studies in Computational Intelligence, 284, 65–74.
Yang, X.S. (2012). “Flower pollination algorithm for global optimization.” In Computer Vision, 7445, 240–249.
Yang, X.S., & Hossein Gandomi, A. (2012). “Bat algorithm: A novel approach for global engineering optimization.” Engineering Computational, 29, 464–483.
Yazdani, M., & Jolai, F. (2015). “Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm.” Journal of Computational Design Engineering, 3, 24–36.
Yu, J.J., & Li, V.O. (2015). “A social spider algorithm for global optimization.” Applied Software Computational, 30, 614–627.
Yuhui, S. (2011). “An optimization algorithm based on brainstorming process.” International Journal of Swarm Intelligence Research, 2, 35–62.
Zhang, C., & Ding, S. (2021). “A stochastic conﬁguration network based on chaotic sparrow search algorithm.” Knowledge-Based Systems, 220(10), 106924.
Zhang, G., & Shi, Y. (2018). “Hybrid sampling evolution strategy for solving single objective bound constrained problems.” In: Proceedings of the 2018 IEEE Congress Evaluation Computational, 1–7.
Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). “Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems.” Applied Mathematical Modelling, 63, 464–490.
Zhao, R., & Tang, W. (2007). “Monkey algorithm for global numerical optimization.” Journal of Uncertain Systems, 2, 165–176.
Zhao, W., Wang, L., & Zhang, Z. (2019a). “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem.” Knowledge-Based Systems, 163, 283–304.
Zhao, W., Wang, L., & Zhang, Z. (2019b). “Artiﬁcial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm.” Neural Computational Applied, 32, 9383–9425.
Zhao, W., Zhang, Z., & Wang, L. (2020). “Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications.” Engineering Applied Artificial Intelligent, 87, 103300.
Zhao, X., Guo, J., & He, M. (2023). “Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification.” Renewable Energy, 217, 119195.
Zheng, Y.J. (2015). “Water wave optimization: A new nature-inspired metaheuristic.” Computational Operation Research, 55, 1–11.
Zhong, C., Li, G., & Meng, Z. (2022). “Beluga whale optimization: a novel nature-inspired metaheuristic algorithm.” Knowledge Based System, 251, 109215.

Metaheuristic Optimization Techniques in Energy Systems: Innovative Algorithms and Application Areas

Year 2024, Volume: 7 Issue: 2, 153 - 171, 14.12.2024

Mert Ökten

https://doi.org/10.51764/smutgd.1542508

Abstract

Optimization is the process of determining the most optimal solution to a problem from among all possible alternatives. In energy systems, metaheuristic optimization algorithms play a significant role in solving complex energy problems. Metaheuristic optimization algorithms are specialized optimization methods developed by drawing inspiration from natural processes, such as genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms are typically used in conjunction with computer-based models. Metaheuristic optimization algorithms can work with large datasets and handle a multitude of variables that need to be optimized under different constraints. Therefore, they are of great importance in the energy sector in terms of sustainability, efficiency, and profitability. These algorithms are used to achieve various objectives in the energy sector, including increasing energy efficiency, reducing energy costs, optimizing different components of energy systems such as energy production, distribution, consumption, and storage, integrating renewable energy sources, and reducing the carbon footprint of energy systems. In this study, the use of metaheuristic optimization algorithms in energy system applications has been examined through examples. Considering the 2532 articles analyzed, it was seen that genetic algorithm (37.4%) and particle swarm optimization (%25.5) were used the most. It has been observed that the use of these algorithms simplifies the solutions to complex problems in various energy-related contexts.

Keywords

Energy systems, Energy efficiency, Metaheuristic optimization algorithms, Sustainability

References

Abbass, H.A. (2001). “MBO: Marriage in honey bees optimisation: A haplometrosis polygynous swarming approach”, In Proceedings of the Congress on Evolutionary Computation—CEC, 27–30 May, (pp. 207–214). Seoul, Korea.
Abdechiri, M., Meybodi, M.R., & Bahrami, H. (2013). “Gases brownian motion optimization: An algorithm for optimization (GBMO).” Applied Software Computational, 13, 2932–2946.
Abdollahzadeh, B., Gharehchopogh, F.S., Khodadadi, N., & Mirjalili, S. (2022). “Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems.” Advances in Engineering Software, 174, 103282.
Abualigah, L., Hanandeh, E.S., Zitar, R.A., Thanh, C.L., Khatir S., & Gandomi, A.H. (2023). “Revolutionizing sustainable supply chain management: A review of metaheuristics.” Engineering Applications of Artificial Intelligence, 126(A), 106839.
Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A.A., Al-Qaness, M.A.A., & Gandomi, A.H. (2021). “Aquila optimizer: a novel meta-heuristic optimization algorithm.” Computational Industry Engineering, 157, 107250.
Aghabegloo, M., Rezaie, K., Torabi, S.A., & Yazdani, M. (2023). “A metaheuristic-driven physical asset risk management framework for manufacturing system considering continuity measures.” Engineering Applications of Artificial Intelligence, 126(A), 106789.
Ahrari, A., & Atai, A.A. (2010). “Grenade explosion method: A novel tool for optimization of multimodal functions.” Applications Software Computational, 10, 1132–1140.
Ala, A., Mahmoudi, A., Mirjalili, S., Simic, V., & Pamucar, D. (2023). “Evaluating the performance of various algorithms for wind energy optimization: A hybrid decision-making model.” Expert Systems with Applications, 221, 119731.
Alsattar, H.A., Zaidan, A.A., & Zaidan, B.B. (2020). “Novel meta-heuristic bald eagle search optimisation algorithm.” Artificial Intelligent Revolution, 53, 2237–2264.
Altay, O. (2022a). “Güncel metasezgisel yöntemlerin standart kalite testi fonksiyonlarında karşılaştırılması.” International Journal Pure Applied Science, 8(2), 286-301.
Altay, O. (2022b). “Chaotic slime mould optimization algorithm for global optimization.” Artificial Intelligence Review, 55, 3979-4040.
Altay, O. & Varol Altay, E. (2022). “Investigation of slime mould algorithm and hybrid slime mould algorithms performance in global optimization problems.” Dicle University Journal of Engineering, 13(4), 661-671.
Altay, O. & Varol Altay, E. (2023). “A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection.” PeerJ Computational Science, 9, 1526.
Ashrafi, S.M., & Dariane, A.B. (2013). “Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody search (MS).” Engineering Applied Artificial Intelligent, 26, 1301–1321.
Askarzadeh, A. (2016). “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm.” Computers & Structures, 169, 1–12.
Atashpaz-Gargari, E., & Lucas, C. (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition.” In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 25–28 September, (pp. 4661–4667). Singapore.
Azizi, M., Aickelin, U., Khorshidi, H.A., & Shishehgarkhaneh, M.B. (2023). “Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization.” Scientific Reports, 13, 226.
Baykasoglu, A., & Senol, M.E. (2016). “Combinatorial optimization via weighted superposition attraction.” In Proceedings of the International Conference on Operations Research of the German Operation Socienty (GOR 2016), 30 August–12 September, Hamburg, Germany.
Bayraktar, Z., Komurcu, M., & Werner, U.H. (2010). “Wind driven optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics.” In Proceedings of the 2010 IEEE Antennas and Propagation Society International Symposium, 11–17 July, (pp. 1–4). Toronto, ON, Canada.
Bhattacharya, A., & Chattopadhyay, P. (2011). “Application of biogeography-based optimisation to solve different optimal power ﬂow problems.” IET Generation Transmission Distribute, 5, 70–80.
Binetti, G., Davoudi, A., Naso, D., Turchiano, B., & Lewis, F.L. (2013). “A distributed auction-based algorithm for the nonconvex economic dispatch problem.” IEEE Transfer Industry Information, 10, 1124–1132.
Braik, M., Sheta, A., & Al-Hiary, H. (2021). “A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm.” Neural Computational Applied, 33, 2515–2547.
Boettcher, S., & Percus, A.G. (2001). “Optimization with extremal dynamics.” Physics Revolution Letter, 86, 5211–5214.
Chen, H., Wang, M., & Zhao, X. (2020). “A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems.” Applied Mathematical Computational, 369, 124872.
Cheng, M.Y., & Prayogo, D. (2014). “Symbiotic organisms search: A new metaheuristic optimization algorithm.” Computers & Structures, 139, 98–112.
Cheraghi, R., & Jahangir, R.H. (2023). “Multi-objective optimization of a hybrid renewable energy system supplying a residential building using NSGA-II and MOPSO algorithms.” Energy Conversion and Management, 294, 117515.
Chicco, G., & Mazza, A. (2019). “Heuristic optimization of electrical energy systems: Reﬁned metrics to compare the solutions.” Sustainable Energy Grids Network, 17, 100197.
Chou, J.S., & Truong, D.N. (2021). “A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean.” Applied Mathematical Computational, 389, 125535.
Chou, J.S., Nguyen, N.M., & Chang, C.P. (2022). “Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning.” Applied Soft Computing, 130, 109642.
Chu, S.C., Tsai, P.W., & Pan, J.S. (2006). “Cat swarm optimization.” In Trends in Artiﬁcial Intelligence (PRICAI 2006), Q. Yang, & G. Webb, (Eds.), Springer, (pp. 854–858).
Civicioglu, P. (2012). “Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm.” Computational Geoscience, 46, 229–247.
Civicioglu, P. (2013a). “Backtracking search optimization algorithm for numerical optimization problems.” Applied Mathematical Computational, 219, 8121–8144.
Civicioglu, P. (2013b). “Artiﬁcial cooperative search algorithm for numerical optimization problems.” Informatics Science, 229, 58–76.
Cuevas, E., Oliva, D., Zaldivar, D., Perez, M.A., Sossa-Azuela, H., & Zaldívar, D. (2012). “Circle detection using electro-magnetism optimization.” Informatics Science, 182, 40–55.
Dai, C., Chen, W., & Zhu, Y. (2006). “Seeker Optimization Algorithm.” In Computational Intelligence and Security (CIS 2006), Y. Wang, Y. Cheung & H. Liu (Eds.), Springer, (pp. 225–229).
Damgacı, E., Boran, K. & Boran, F.E. (2017). “Sezgisel bulanık TOPSIS yöntemi kullanarak Türkiye’nin yenilenebilir enerji kaynaklarının değerlendirilmesi.” Politeknik Dergisi, 20(3), 629-637.
De Castro, L., Von Zuben, C.J., & De Castro, L.N. (2002). “Learning and optimization using the clonal selection principle.” IEEE Transmission Evolution Computer, 6, 239–251.
Değer, K., Özkaya, M.G., & Boran, F.E. (2023). “Modelling and analysis of future energy scenarios on the sustainability axis.” Journal of Polytechnic, 26(2), 665-678.
Demir, F.B., Tuncer, T., & Kocamaz, A.F. (2020). “A chaotic optimization method based on logistic-sine map for numerical function optimization.” Neural Computing and Applications, 32(17), 14227–14239.
Detwal, P.K., Agrawal, R., Samadhiya, A., & Kumar, A. (2023). “Metaheuristics in circular supply chain intelligent systems: A review of applications journey and forging a path to the future.” Engineering Applications of Artificial Intelligence, 126(D), 107102.
Dhiman, G., & Kumar, D. (2019). “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems.” Knowledge-Based System, 165, 169–196.
Dhiman, G., Garg, M., Nagar, M., Kumar, V., & Dehghani, M. (2021). “A novel algorithm for global optimization: Rat swarm optimizer.” Journal of Ambient Intelligence Humanization Computational, 12, 8457–8482.
Doering, J., Kizys, R., Juan, A.A., Fito, A., & Polat, O. (2019). “Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends.” Operations Research Perspectives, 6, 100121.
Dogan, B., & Olmez, T. (2015). “A new metaheuristic for numerical function optimization: Vortex search algorithm.” Informatics Science, 293, 125–145.
Dokeraglu, T., Deniz, A., & Kiziloz, H.E. (2022). “A comprehensive survey on recent metaheuristics for feature selection.” Neurocomputing, 494, 269-296.
Dorigo, M., Maniezzo, V., & Colorni, A. (1991). “Positive feedback as a search strategy. Politecnico di Milano: Dipartimento di Elettronica.” Technical Report, 91, 16.
Duan, H., & Qiao, P. “Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning.” International Journal of Intelligence Computer Cybernetics, 7, 24–37.
El-Abd, M. (2013). “An improved global-best harmony search algorithm.” Applied Mathematical Computational, 222, 94–106.
Emami, H., & Derakhshan, F. (2015). “Election algorithm: A new socio-politically inspired strategy.” AI Community, 28, 591–603.
Erlich, I., Venayagamoorthy, G.K., & Worawat, N. (2010) “A mean-variance optimization algorithm.” In Proceedings of the 2010 IEEE World Congress on Computational Intelligence, 18–23 July, Barcelona, Spain.
Eskandar, H., Sadollah, A., Bahreininejad, A. & Hamdi, M. (2012). “Water cycle algorithm: A novel metaheuristic optimization method for solving constrained engineering optimization problems.” Computers & Structures, 110, 151–166.
Eusuff, M.M., & Lansey, K.E. (2003). “Optimization of water distribution network design using the shuffled frog leaping algorithm.” Journal of Water Resource Planning Management, 129, 210–225.
Eusuff, M., Lansey, K.E., & Pasha, F. (2006). “Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization.” Engineering Optimization, 38, 129–154.
Fanian, F., & Rafsanjani, M.K. (2023). “CFMCRS: Calibration fuzzy-metaheuristic clustering routing scheme simultaneous in on-demand WRSNs for sustainable smart city.” Expert Systems with Applications, 211, 118619.
Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020a). “Equilibrium optimizer: A novel optimization algorithm.” Knowledge-Based Systems, 191, 105190.
Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A.H. (2020b). “Marine predators algorithm: A nature-inspired metaheuristic.” Expert Systems Applied, 152, 113377.
Farmer, J., Packard, N.H., & Perelson, A.S. (1986). “The immune system, adaptation, and machine learning.” Physical Differential Nonlinear Phenomena, 22, 187–204.
Feng, Z.K., Niu, W.J., & Liu, S. (2021). “Cooperation search algorithm: A novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems.” Applied Soft Computing, 98, 106734.
Feo, T.A., & Resende, M.G. (1989). “A probabilistic heuristic for a computationally difficult set covering problem.” Operational Research Letter, 8, 67–71.
Fogel, D.B. (2009). Artiﬁcial Intelligence through Simulated Evolution. Wiley, New York, USA. Gandomi, A.H., & Alavi, A.H. (2012). “Krill herd: A new bio-inspired optimization algorithm.” Community Nonlinear Science Numerical Simulation, 17, 4831–4845.
Ghalizadeh, H., Goh, M., Fazlollahtabar, H., & Mamashli, Z. (2022). “Modelling uncertainty in sustainable-green integrated reverse logistics network using metaheuristics optimization.” Computers & Industrial Engineering, 163, 107828.
Ghasemi-Marzbali, A. (2020). “A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm.” Software Computation, 24, 13003–13035.
Glover, F. (1977). “Heuristics for integer programming using surrogate constraints.” Decision Science, 8, 156–166.
Glover, F. (1989). “Tabu Search: Part I.” ORSA Journal of Computational, 1, 190–206.
Greensmith, J., Aickelin, U., & Cayzer, S. (2000). “Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection.” In Haptics: Science, Technology, Applications, 3627, 153–167.
Guang, Q., Feng, L., Lijuan, L., Lu, J.W.Z., Leung, A.Y.T., Lu, V.P., & Mok, K.M. (2010). “A quick group search optimizer and its application to the optimal design of double layer grid shells.” AIP Publishing, 1233, 718.
Hansen, N., Müller, S.D., & Koumoutsakos, P. (2013). “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES).” Evolution Computer, 11, 1–18.
Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W., & Mirjalili, S. (2019). “Henry gas solubility optimization: A novel physics based algorithm.” Futurist General Computational Systems, 101, 646–667.
Hashim, F.A., Hussain, K., Houssein, E.H., Mabrouk, M.S., & Al-Atabany, W. (2021). “Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems.” Applied Intelligence, 51, 1531–1551.
Hashim, F.A., & Hussien, A.G. (2022). “Snake optimizer: A novel metaheuristic optimization algorithm.” Knowledge-Based Systems, 242, 108320.
Hayyolalam, V., & Pourhaji Kazem, A.A. (2020). “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems.” Engineering Applied Artificial Intelligence, 87, 103249.
He, S., Wu, Q., & Saunders, J. (2006). “A novel group search optimizer inspired by animal behavioral ecology.” In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, 16–21 July, Vancouver, BC, Canada.
Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). “Harris hawks optimization: Algorithm and applications.” Futurist General Computational Systems, 97, 849–872.
Higashitani, M., Ishigame, A., & Yasuda, K. (2006). “Particle swarm optimization considering the concept of predator-prey behavior.” In Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, 16–21 July, (pp. 434–437). Vancouver, BC, Canada.
Holland, J.H. (1992). Adaptation in Natural and Artiﬁcial Systems, MIT Press, Cambridge, MA, USA.
Hosseini, H.S. (2007). “Shah Problem solving by intelligent water drops.” In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 25–28 September, (pp. 3226–3231). Singapore.
Hosseini, H.S. (2011). “Principal components analysis by the galaxy-based search algorithm: A novel metaheuristic for continuous optimization.” International Journal of Computational Science Engineering, 6, 132–140.
Iqbal, M., Azam, M., Naeem, M., Khwaja, A. S. & Anpalagan, A. (2014) “ Optimization classification, algorithms and tools for renewable energy: A review” Renewable and Sustainable Energy Reviews, 39, 640–654.
Jain, M., Singh, V., & Rani, A. (2019). “A novel nature-inspired algorithm for optimization: Squirrel search algorithm.” Swarm Evolution Computational, 44, 148–175.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). “Ions motion algorithm for solving optimization problems.” Applied Software Computational, 32, 72–79.
Kaboli, S.H.A., Selvaraj, J., & Rahim, N. (2017). “Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems.” Journal of Computer Science, 19, 31–42.
Karaboga, D., & Basturk, B.A. (207). “Powerful and efficient algorithm for numerical function optimization: Artiﬁcial bee colony (ABC) algorithm.” Journal of Global Optimization, 39, 459–471.
Karami, H., Sanjari, M.J., & Gharehpetian, G.B. (2014). “Hyper-spherical search (HSS) algorithm: A novel meta-heuristic algorithm to optimize nonlinear functions.” Neural Computational Applied, 25, 1455–1465.
Kashan, A.H. (2014). “League championship algorithm (LCA): An algorithm for global optimization inspired by sport championships.” Applied Software Computational, 16, 171–200.
Kashan, A.H. (2015). “A new metaheuristic for optimization: Optics inspired optimization (OIO).” Computational Operation Research, 55, 99–125.
Kaveh, A., & Talatahari, S. (2010). “A novel heuristic optimization method: Charged system search.” Acta Mechanical, 213, 267–289.
Kaveh, A., & Khayatazad, M. (2012). “A new meta-heuristic method: Ray optimization.” Computers & Structures, 112–113, 283–294.
Kaveh, A., & Farhoudi, N. (2013). “A new optimization method: Dolphin echolocation.” Advance Engineering Software, 59, 53–70.
Kaveh, A., & Mahdavi, V. (2014). “Colliding bodies optimization: A novel meta-heuristic method.” Computers & Structures, 139, 18–27.
Kaveh, A., & Dadras, A. (2017). “A novel meta-heuristic optimization algorithm: Thermal exchange optimization.” Advance Engineering Software, 110, 69–84.
Khan, A.A., Laghari, A.A., Gadekallu, T.R., Shaikh, Z.A., Javed, A.R., Rashid, M., Estrela, V.V., & Mikhaylov, A. (2022). “A drone-based data management and optimization using metaheuristic algorithms and blockchain smart contracts in a secure fog environment.” Computers and Electrical Engineering, 102, 108234.
Khelili, M.A., Slatnia, S., Kazar, O., Merizig, A., & Mirjalili, S. (2023). “Deep learning and metaheuristics application in internet of things: A literature review.” Microprocessors and Microsystems, 98, 104792.
Kiran, M.S., & Kiran, M.S. (2015). “TSA: Tree-seed algorithm for continuous optimization.” Experimental System Applications, 42, 6686–6698.
Kinost, A., Doerner, K.F., & Rinderle-Ma, S. (2022). “Combining metaheuristics and process mining: Improving cobot placement in a combined cobot assignment and job shop scheduling problem.” Procedia Computer Science, 200, 1836-1845.
Kirkpatrick, S., Gelatt, J.C.D., & Vecchi, M.P. (1986). “Optimization by simulated annealing.” World Scientiﬁc Lecture Notes in Physics, 220, 339–348.
Klar, M., Glatt M., & Aurich, J.C. (2023). “Performance comparison of reinforcement learning and metaheuristics for factory layout planning.” CIRP Journal of Manufacturing Science and Technology, 45, 10-25.
Krishnanand, K., & Ghose, D. (2005). “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics.” In Proceedings of the 2005 IEEE Swarm Intelligence Symposium, 8–10 June, (pp. 84–91). Pasadena, CA, USA.
Kulkarni, A.J., Durugkar, I.P., & Kumar, M. (2013). “Cohort Intelligence: A self-supervised learning behavior.” In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, 13–16 October, (pp. 1396–1400). Manchester, UK.
Kumar, A., Misra, R.K., & Singh, D. (2017). “Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase.” In: Proceedings of the 2017 IEEE Congress Evolution Computer, 1835–1842.
Kumar, N., Singh, N., & Vidyarthi, D.P. (2021). “Artificial lizard search optimization (ALSO): A novel nature-inspired metaheuristic algorithm.” Software Computational, 25, 6179–6201.
Kutlu Onay, F., & Aydemir, S.B. (2022). “Chaotic hunger games search optimization algorithm for global optimization and engineering problems.” Mathematics and Computers in Simulation, 192(10), 514–536.
Lam, A.Y.S., & Li, V.O.K. (2010). “Chemical-reaction-inspired metaheuristic for optimization.” IEEE Transmission Evolution Computer, 14, 381–399.
Lee, K.Y., & Vale, Z.A. (2020). Applications of Modern Heuristic Optimization Methods in Power and Energy Systems. Wiley: Hoboken, NJ, USA.
Lessmann, S., Caserta, M., & Arango, I.M. (2011). “Tuning metaheuristics: A data mining based approach for particle swarm optimization.” Expert Systems with Applications, 38(10), 12826-12838.
Li, M.D., Zhao, H., Weng, X.W., & Han, T. (2016). “A novel nature-inspired algorithm for optimization: Virus colony search.” Advance Engineering Software, 92, 65–88.
Li, T., Wang, C.F., Wang, W.B., & Su, W.L. (2005). “A global optimization bionics algorithm for solving integer programming-plant growth simulation algorithm.” Systems Engineering-Theory Practical, 25, 76–85.
Li, X.D., Wang, J.S., Hao, W.K., Zhang, M., & Wang, M. (2022). “Chaotic arithmetic optimization algorithm.” Applied Intelligence, 52(14), 16718–16757.
Liang, Y.C., & Juarez, J.R.C. (2016). “A novel metaheuristic for continuous optimization problems: Virus optimization algorithm.” Engineering Optimization, 48(1), 73–93.
Luo, J., Chen, H., Zhang, Q., Xu, Y., Huang, H., & Zhao, X. (2018). “An improved grasshopper optimization algorithm with application to financial stress prediction.” Applied Mathematical Modelling, 64, 654–668.
MATLAB. (2018). R2019b. The MathWorks Inc., Natick, Massachusetts, USA.
Mehrabian, A., & Lucas, C. (2006). “A novel numerical optimization algorithm inspired from weed colonization.” Ecological Information, 1, 355–366.
Mendi, F., Başkal, T., Boran. K., & Boran, F.E. (2010). “Optimization of module, shaft diameter and rolling bearing for spur gear through algorithm.” Energy Systems with Applications, 37, 8058-8064.
Meng, A.B., Chen, Y.C., Yin, H., & Chen, S.Z. (2014). “Crisscross optimization algorithm and its application.” Knowledge-Based Systems, 67, 218–229.
Mihaly, N.B., Luca, A.V., Simon Varhelyi, M., & Cristea, V.M. (2023). “Improvement of air flowrate distribution in the nitrification reactor of the waste water treatment plant by effluent quality, energy and greenhouse gas emissions optimization via artificial neural networks models.” Journal of Water Process Engineering, 54, 103935.
Mirjalili, S. (2013). “Dragonﬂy algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems.” Neural Computational Applied, 27, 1053–1073.
Mirjalili, S., Mirjalili, S.M., & Lewis, A. (2014). “Grey wolf optimizer.” International Journal of Advance Engineering Software, 69, 46–61.
Mirjalili, S. (2015a). “Moth-ﬂame optimization algorithm: A novel nature-inspired heuristic paradigm.” Knowledge-Based Systems, 89, 228–249.
Mirjalili, S. (2015b). “The ant lion optimizer.” Advance Engineering Software, 83, 80–98.
Mirjalili, S. (2016). “SCA: A sine cosine algorithm for solving optimization problems.” Knowledge-Based Systems, 96, 120–133.
Mirjalili, S., & Lewis, A. (2016). “The whale optimization algorithm.” Advance Engineering Software, 95, 51–67.
Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., & Mirjalili, S.M. (2017). “Salp swarm algorithm: A bio-inspired optimizer for engineering design problems.” Advance Engineering Software, 114, 163–191.
Mladenovi ´C, N., & Hansen, P. (1997). “Variable neighborhood search.” Computational Operational Research, 24, 1097–1100.
Moein, S., & Logeswaran, R. (2014). “KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules.” Informatics Science, 275, 127–144.
Moghdani, R., & Salimifard, K. (2018). “Volleyball premier league algorithm.” Applied Software Computational, 64, 161–185.
Mohamed, A.W., Hadi, A.A., & Mohamed, A.K. (2020). “Gaining-sharing knowledge based algorithm for solving optimization problems: A novel nature-inspired algorithm.” International Journal of Machine Learning and Cybernetics, 11, 1501–1529.
Mohammed, H.M., & Rashid, T.A. (2021). “Chaotic ﬁtness-dependent optimizer for planning and engineering design.” Software Computing, 25(22), 14281–14295.
Monismith, D.R., & Mayﬁeld, B.E. (2008). “Slime mould as a model for numerical optimization.” In Proceedings of the IEEE Swarm Intelligence Symposium, 21–23 September, St. Louis, MO, USA.
Moosavian, N., & Roodsari, B.K. (2014). “Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks.” Swarm Evolution Computational, 17, 14–24.
Moscato, P. (1989). “On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms.” In Caltech Concurrent Computation Program (Report 826), California Institute of Technology, pp. 158–179, Pasadena, CA, USA.
Muthiah-Nakarajan, V., & Noel, M.M. (2016). “Galactic swarm optimization: A new global optimization metaheuristic inspired by galactic motion.” Applied Software Computational, 38, 771–787.
Mühlenbein, H., & Pass, G. (1996). “From recombination of genes to the estimation of distributions I. Binary parameters.” In Computer Vision, 1141, 178–187.
Narayanan, A., & Moore, M. (1996). “Quantum-inspired genetic algorithms.” In Proceedings of the IEEE International Conference on Evolutionary Computation ICEC-96, 20–22 May, (pp. 61–66). Nagoya, Japan.
Naruei, I., & Keynia, F. (2021). “A new optimization method based on coot bird natural life model.” Expert Systems with Applications, 115352.
Nassef, A.M., Abdelkareem, M.A., Maghrabie, H.M., & Baroutaji, A. (2023). “Review of metaheuristic optimization algorithms for power systems problems.” Sustainability, 15, 9434.
Pan, W.T. (2012) “A new fruit fly optimization algorithm: Taking the ﬁnancial distress model as an example.” Knowledge-Based Systems, 26, 69–74.
Passino, K. (2002). “Biomimicry of bacterial foraging for distributed optimization and control.” IEEE Controlling Systems, 22, 52–67.
Pelikan, M., Goldberg, M.E., & Cant-Paz, E. (1999). “BOA: The Bayesian optimization algorithm.” In Proceedings of the Genetic and Evolutionary Computation Conference—GECCO-99, I, 13–17 July, (pp. 525–532). Orlando, FL, USA.
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020a). “An improved moth- flame optimization algorithm with hybrid search phase.” Knowledge-Based Systems, 191, 105277.
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020b). “Improving exploration and exploitation via a hyperbolic gravitational search algorithm.” Knowledge-Based Systems, 193, 105404.
Pereira, J.L.J. (2021). “Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization.” Expert Systems Applied, 170, 114522.
Pierezan, J., & Coelho, L.D.S., (2018). “Coyote optimization algorithm: A new metaheuristic for global optimization problems.” In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), (pp. 1–8). 8–13 July, Rio de Janeiro, Brasil.
Poli, R., Kennedy, J., & Blackwell, T. (2007). “Particle swarm optimization.” Swarm Intelligent, 1, 33–57. Punnathanam, V., & Kotecha, P. (2016). “Yin-yang-pair optimization: A novel light weight optimization algorithm.” Engineering Applied Artificial Intelligence, 54, 62–79.
PYTHON. (1995). Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands.
Qais, M.H., Hasanien, H.M., & Alghuwainem, S. (2020). “Transient search optimization: A new meta-heuristic optimization algorithm.” Applied Intelligence, 50(11), 3926–3941.
Qu, G., Cheng, H., Yao, L., Ma, Z., & Zhu, Z. (2010). “Transmission surplus capacity based power transmission expansion planning.” Electrical Power Systems Research, 80, 19–27.
Rabanal, P., Rodríguez, L., & Rubio, F. (2007). “Using river formation dynamics to design heuristic algorithms in swarm.” Evolutionary, and Memetic Computational, 4618, 163–177.
Radosavljević, J. (2018). Metaheuristic Optimization in Power Engineering. The Institution of Engineering and Technology, British Library Cataloguing in Publication Data, Herts, United Kingdom.
Rahmani, R., & Yusof, R. (2014). “A new simple, fast and efficient algorithm for global optimization over continuous search-space problems.” Applied Mathematical Computational, 248, 287–300.
Rahul, K., & Banyal, R.K. (2022). “Metaheuristics approach to improve data analysis process for the healthcare sector.” Procedia Computer Science, 215, 98-103.
Rahkar Farshi, T. (2021). “Battle royale optimization algorithm.” Neural Computational Applied, 33, 1139–1157.
Rakhshani, H., & Rahati, A. (2017). “Snap-drift cuckoo search: A novel cuckoo search optimization algorithm.” Applied Software Computational, 52, 771–794.
Rao, R.V., Savsani, V.J., & Vakharia, D. (2011). “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems.” Computational Deserve, 43, 303–315.
Rao, R.V. (2016). “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems.” International Journal of Industry Engineering Computational, 7, 19–34.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). “GSA: A gravitational search algorithm.” Informatics Science, 179, 2232–2248.
Ray, T., & Liew, K. (2003). “Society and civilization: An optimization algorithm based on the simulation of social behavior.” IEEE Transfer Evolution Computational, 7, 386–396.
Rechenberg, I. (1971). “Evolutionsstrategie–optimierung technischer systeme nach prinzipien der biologischen evolution (in German).” [Ph.D. Thesis, Technical University of Berlin].
Reynolds, R.G. (1994). “An introduction to cultural algorithms.” In Proceedings of the Third Annual Conference on Evolutionary Programming, 24–26 February, (pp. 131–139). San Diego, CA, USA.
Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). “Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems.” Applied Software Computational, 13, 2592–2612.
Sakthivel, S., Pandiyan, S.A., Marikani, S., & Selvi, S.K. (2013). “Application of big-bang big-crunch algorithm for optimal power flow problems.” International Journal of Engineering Science, 2, 41–47.
Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J.A. (2014). “The coral reefs optimization algorithm: A novel metaheuristic for efficiently solving optimization problems.” Science World Journal, 1–15.
Salgotra, R., Singh, U., Singh, G., Mittal, N., & Gandomi, A.H. (2021). “A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems.” Computational Methods Applied Mechanical Engineering, 383, 113916.
Salimi, H. (2015). “Stochastic fractal search: A powerful metaheuristic algorithm.” Knowledge-Based Systems, 75, 1–18.
Saremi, S., Mirjalili, S., & Lewis, A. (2017). “Grasshopper optimisation algorithm: Theory and application.” Advance Engineering Software, 105, 30–47.
Satapathy, S.C., & Naik, A. (2016). “Social group optimization (SGO): A new population evolutionary optimization technique.” Complex Intelligence Systems, 2, 173–203.
Savsani, P., & Savsani, V. (2016). “Passing vehicle search (PVS): A novel metaheuristic algorithm.” Applied Mathematical Modelling, 40, 3951–3978.
Sayed, G.I., Darwish, A., & Hassanien, A.E. (2018). “A new chaotic multi-verse optimization algorithm for solving engineering optimization problems.” Journal of Experimental and Theoretical Artiﬁcial Intelligence, 30(2), 293–317.
Shayanfar, H., & Gharehchopogh, F.S. (2018). “Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems.” Applied Software Computational, 71, 728–746.
Shirke, C., Sabar, N., Chung, E., & Bhaskar, A. (2021). “Metaheristic approach for designing robust traffic signal timings to effectively serve varying traffic demand.” Journal of Intelligent Transportation Systems, 26(3), 343-355.
Storn, R. & Price, K. (1997). “Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces.” Journal of Global Optimization, 11, 341–359.
Sulaiman, M.H., & Mustaffa, Z. (2023). “Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building.” Journal of Building Engineering, 76, 107139.
Sulaiman, M.H., Mustaffa, Z., Saari, M.M., Daniyal, H., & Mirjalili, S. (2023). “Evolutionary mating algorithm.” Neural Computational Applied, 35(1), 487–516.
Tan, Y., & Zhu, Y. (2010). “Fireworks algorithm for optimization.” In Computer Vision, 6145, 355–364.
Tang, R., Fong, S., Yang, X.S., & Deb, S. (2012). “Wolf search algorithm with ephemeral memory.” In Proceedings of the Seventh International Conference on Digital Information Management (ICDIM 2012), 22–24 August, (pp. 165–172). Macau, China.
Tarkhaneh, O., Alipour, N., Chapnevis, A., & Shen, H. (2021). “Golden tortoise beetle optimizer: A novel nature-inspired meta-heuristic algorithm for engineering problems.” Arxiv. https://arxiv.org/pdf/2104.01521.pdf
Topal, A.O., & Altun, O. (2016). “A novel meta-heuristic algorithm: dynamic virtual bats algorithm.” Informatics Science, 354, 222–235.
Uymaz, S.A., Tezel, G., & Yel, E. (2015). “Artiﬁcial algae algorithm (AAA) for nonlinear global optimization.” Applied Software Computational, 31, 153–171.
Varol Altay, E., & Alatas, B. (2020). “Bird swarm algorithms with chaotic mapping.” Artiﬁcial Intelligence Review, 53(2), 1373–1414.
Varol Altay, E. & Alatas, B. (2021). “Differential evolution and sine cosine algorithm based novel hybrid multiobjective approaches for numerical association rule mining.” Information Sciences, 554, 198-221.
Varol Altay, E. & Altay, O. (2021). “Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması.” Dicle University Journal of Engineering, 12(5), 729-741.
Varol Altay, E., Gurgenc, E., Altay, O., & Dikici, A. (2022). “Hybrid artificial neural network based on a metaheuristic optimization algorithm for the prediction of reservoir temperature using hydrogeochemical data of different geothermal areas in Anatolia (Turkey).” Geothermics, 104, 102476.
Varol Altay, E. & Altay, O. (2023a). “Assessment of grey wolf optimizer and its variants on benchmark functions”, International Conference on Computing, Intelligence and Data Analytics (ICCIDA) 2022: Computational Intelligence, Data Analytics and Applications. (pp. 55-66), Springer.
Varol Altay, E. & Altay, O. (2023b) “A novel hybrid multilayer perceptron neural network with improved grey wolf optimizer.” Neural Computing and Applications, 35, 529-556.
Wang, G.G., Deb, S., & Coelho, L.D.S. (2015a). “Elephant herding optimization.” In Proceedings of the 3rd International Symposium on Computational and Business Intelligence, 7-9 December, (pp. 1–5). Bali, Indonesia.
Wang, G.G., Deb, S., & Cui, Z. (2015b). “Monarch butterﬂy optimization.” Neural Computational Applied, 31, 1995–2014.
Wang, Q.Y., Lv, X.L., & Zeman, A. (2023). “Optimization of a multi-energy microgrid in the presence of energy storage and conversion devices by using an improved gray wolf algorithm.” Applied Thermal Engineering, 234, 121141.
Wang, Z., Chen, L., Wang, B., Huang, L., Wang, K., & Ma, R. (2023). “Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors.” Energy, 282, 128795.
Witten, T.A., & Sander, L.M. (1981) “Diffusion-limited aggregation: A kinetic critical phenomenon.” Physical Revolution Letter, 47, 1400–1403.
Xian, S., Zhang, J., Xiao, Y., & Pang, J. (2017). “A novel fuzzy time series forecasting method based on the improved artiﬁcial ﬁsh swarm optimization algorithm.” Software Computational, 22, 3907–3917.
Yampolskiy, R.V., Ashby, L., & Hassan, L. (2012). “Wisdom of artificial crowds: A metaheuristic algorithm for optimization.” Journal of Intelligence Learning Systems Applications, 4(2), 10.
Yang, L., Gao, S., Yang, H., Cai, Z., Lei, Z., & Todo, Y. (2021). “Adaptive chaotic spherical evolution algorithm.” Memetic Computing, 13(3), 383–411.
Yang, X.S., & Deb, S. (2009). ”Cuckoo search via lévy ﬂights.” In Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 9–11 December, (pp. 210–214). Coimbatore, India.
Yang, X.S., & Deb, S. (2010). “Eagle strategy using lévy walk and fireﬂy algorithms for stochastic optimization.” In Studies in Computational Intelligence, 284, 101–111.
Yang, X.S. (2010a). “Fireﬂy algorithm, stochastic test functions and design optimization.” International Journal Bio-Inspired Computational, 2, 78.
Yang, X.S. (2010b). “A New Metaheuristic Bat-Inspired Algorithm.” In Studies in Computational Intelligence, 284, 65–74.
Yang, X.S. (2012). “Flower pollination algorithm for global optimization.” In Computer Vision, 7445, 240–249.
Yang, X.S., & Hossein Gandomi, A. (2012). “Bat algorithm: A novel approach for global engineering optimization.” Engineering Computational, 29, 464–483.
Yazdani, M., & Jolai, F. (2015). “Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm.” Journal of Computational Design Engineering, 3, 24–36.
Yu, J.J., & Li, V.O. (2015). “A social spider algorithm for global optimization.” Applied Software Computational, 30, 614–627.
Yuhui, S. (2011). “An optimization algorithm based on brainstorming process.” International Journal of Swarm Intelligence Research, 2, 35–62.
Zhang, C., & Ding, S. (2021). “A stochastic conﬁguration network based on chaotic sparrow search algorithm.” Knowledge-Based Systems, 220(10), 106924.
Zhang, G., & Shi, Y. (2018). “Hybrid sampling evolution strategy for solving single objective bound constrained problems.” In: Proceedings of the 2018 IEEE Congress Evaluation Computational, 1–7.
Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). “Queuing search algorithm: a novel metaheuristic algorithm for solving engineering optimization problems.” Applied Mathematical Modelling, 63, 464–490.
Zhao, R., & Tang, W. (2007). “Monkey algorithm for global numerical optimization.” Journal of Uncertain Systems, 2, 165–176.
Zhao, W., Wang, L., & Zhang, Z. (2019a). “Atom search optimization and its application to solve a hydrogeologic parameter estimation problem.” Knowledge-Based Systems, 163, 283–304.
Zhao, W., Wang, L., & Zhang, Z. (2019b). “Artiﬁcial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm.” Neural Computational Applied, 32, 9383–9425.
Zhao, W., Zhang, Z., & Wang, L. (2020). “Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications.” Engineering Applied Artificial Intelligent, 87, 103300.
Zhao, X., Guo, J., & He, M. (2023). “Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification.” Renewable Energy, 217, 119195.
Zheng, Y.J. (2015). “Water wave optimization: A new nature-inspired metaheuristic.” Computational Operation Research, 55, 1–11.
Zhong, C., Li, G., & Meng, Z. (2022). “Beluga whale optimization: a novel nature-inspired metaheuristic algorithm.” Knowledge Based System, 251, 109215.

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Primary Language	Turkish
Subjects	Optimization Techniques in Mechanical Engineering
Journal Section	Articles
Authors	Mert Ökten 0000-0003-0077-4471
Early Pub Date	November 4, 2024
Publication Date	December 14, 2024
Submission Date	September 2, 2024
Acceptance Date	October 25, 2024
Published in Issue	Year 2024 Volume: 7 Issue: 2

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APA	Ökten, M. (2024). Enerji Sistemlerinde Metasezgisel Optimizasyon Teknikleri: Yenilikçi Algoritmalar ve Uygulama Alanları. Sürdürülebilir Mühendislik Uygulamaları Ve Teknolojik Gelişmeler Dergisi, 7(2), 153-171. https://doi.org/10.51764/smutgd.1542508

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