Diferansiyel Evrim Algoritması Temelli Global Arama Stratejisi ile Geliştirilmiş Yeni Bir Yapay Denizanası Arama Algoritması

Year 2022, , 1178 - 1192, 31.12.2022

Gülnur Yıldızdan

https://doi.org/10.31202/ecjse.1131734

Abstract

Metasezgisel algoritmalar, bir problemi çözmek için olası çözümlerden daha etkili olanına karar vermek için kullanılan ve doğal fenomenlerden esinlenen algoritmalardır. Her geçen gün sayıları artmakta olan bu algoritmalar, kesin çözümü garanti etmemesine rağmen kesin çözüm etrafındaki bir çözüme hızlı şekilde ulaşmayı vadeder. Yapay Denizanası Arama Algoritması(YDA) da 2021 yılında önerilmiş yeni bir metasezgisel algoritmadır. Bu çalışmada, YDA’nın global arama kabiliyetini geliştirmek amacıyla standart algoritmanın global arama bölümünde bir düzenleme yapılmıştır. Buna göre, Diferansiyel Evrim Algoritmasındaki başarılı mutasyon stratejilerinden biri olan “current-to-best” yaklaşımı, YDA’nın global arama yöntemine entegre edilmiştir. Bu düzenleme sonucu elde edilen gelişmiş algoritma(MYDA),yedi tanesi tek modlu, beş tanesi çok modlu özellikte olmak üzere toplam on iki kıyaslama fonksiyonu üzerinde 10,30,50,100,500 ve 1000 boyut için test edilmiştir. Ayrıca MYDA, literatürden seçilen algoritmalarla da karşılaştırılmıştır. Sonuçlar istatistik testler yardımıyla yorumlanmıştır. Elde edilen sonuçlar incelendiğinde, önerilen algoritmanın tüm fonksiyonlarda tüm boyutlar için standart algoritmadan daha iyi performans gösterdiği tespit edilmiştir. Literatürle yapılan karşılaştırmada ise algoritmanın başarılı ve yarışmacı sonuçlar ürettiği belirlenmiştir.

Keywords

Diferansiyel evrim algoritması, metasezgisel algoritma, sürekli optimizasyon, yapay denizanası arama algoritması, klasik kıyaslama fonksiyonları

A Novel Artificial Jellyfish Search Algorithm Improved with a Differential Evolution Algorithm-Based Global Search Strategy

Abstract

Metaheuristic algorithms are algorithms inspired by natural phenomena and that are used to decide which possible solution is more efficient to solve a problem. Although these algorithms, whose numbers are increasing day by day, do not guarantee the exact solution, they promise to reach a solution around the exact solution quickly. Artificial Jellyfish Search Algorithm (YDA) is also a new metaheuristic algorithm proposed in 2021. In this study, a modification has been made to the global search part of the standard algorithm in order to improve the global search capability of YDA. Accordingly, the "current-to-best" approach, which is one of the successful mutation strategies in the Differential Evolution Algorithm, has been integrated into the global search method of YDA. The advanced algorithm (MYDA) obtained as a result of this modification has been tested for 10,30,50,100,500 and 1000 dimensions on a total of twelve benchmark functions, seven of which are uni-modal and five are multi-modal. In addition, MYDA has also been compared with algorithms selected from the literature. The results have been interpreted with the help of statistical tests. When the results obtained are examined, it has been determined that the proposed algorithm outperforms the standard algorithm for all dimensions in all functions. In the comparison with the literature, it has been determined that the algorithm produces successful and competitive results.

Keywords

Differential evolution algorithm, metaheuristic algorithm, continuous optimization, artificial jellyfish search algorithm, classic benchmark functions

References

• Akay, B., Nümerik optimizasyon problemlerinde yapay arı kolonisi (artıfıcıal bee colony) algoritmasının performans analizi, 2009.
• Chou, J.-S.,Truong, D.-N., A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean, Applied Mathematics and Computation, 2021, 389: p. 125535.
• Price, K.V., Storn, R.M., Lampinen, J.A., The differential evolution algorithm, Differential evolution: a practical approach to global optimization, 2005: p. 37-134.
• Gouda, E.A., Kotb, M.F., El-Fergany, A.A., Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis, Energy, 2021, 221: p. 119836.
• Shaheen, A.M., Elsayed, A.M., Ginidi, A.R., Elattar, E.E., El-Sehiemy, R.A., Effective automation of distribution systems with joint integration of DGs/SVCs considering reconfiguration capability by jellyfish search algorithm, IEEE Access, 2021, 9: p. 92053-92069.
• Ginidi, A., Elsayed, A., Shaheen, A., Elattar, E., El-Sehiemy, R., An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids, Mathematics, 2021, 9(17): p. 2053.
• Yıldızdan, G., Baykan, Ö.K., A Novel Artificial Jellyfish Search Algorithm Improved with Detailed Local Search Strategy, in 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021: IEEE.
• Kaveh, A., Biabani Hamedani, K., Kamalinejad, M., Joudaki, A., Quantum-based jellyfish search optimizer for structural optimization, دانشگاه علم و صنعت ایران, 2021, 11(2): p. 329-356.
• Jiang, S.-J., Dao, T.-K., Vu, V.-D., Ngo, T.-G., A Power System Economic Load Dispatch Using Jellyfish Search Algorithm, in Soft Computing for Problem Solving. 2021, Springer. p. 321-331.
• Chou, J.-S.,Truong, D.-N., Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems, Chaos, Solitons & Fractals, 2020, 135: p. 109738.
• Bujok, P., Three Steps to Improve Jellyfish Search Optimiser, in MENDEL, 2021.
• Abdel-Basset, M., Mohamed, R., Abouhawwash, M., Chakrabortty, R.K., Ryan, M.J., Nam, Y., An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations, 2021.
• Huang, R.,Lin, Y., A Maximum Power Point Tracking Strategy for Photovoltaic System Based on Improved Artificial Jellyfish Search Optimizer, in 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 2021: IEEE.
• Youssef, H., Hassan, M.H., Kamel, S., Elsayed, S.K., Parameter Estimation of Single Phase Transformer Using Jellyfish Search Optimizer Algorithm, in 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), 2021: IEEE.
• Rajpurohit, J., A Modified Jellyfish Search Optimizer with Opposition Based Learning and Biased Passive Swarm Motion, Ingénierie des Systèmes d'Information, 2021, 26(6).
• Rajpurohit, J.,Sharma, T.K., Chaotic active swarm motion in jellyfish search optimizer, International Journal of System Assurance Engineering and Management, 2022: p. 1-17.
• Gandomi, A.H., Yang, X.-S., Talatahari, S., Alavi, A.H., Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 2013, 18(1): p. 89-98.
• Keskintürk, T., Diferansiyel gelişim algoritması, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 2006, 5(9): p. 85-99.
• Mallipeddi, R., Suganthan, P.N., Pan, Q.-K., Tasgetiren, M.F., Differential evolution algorithm with ensemble of parameters and mutation strategies, Applied soft computing, 2011, 11(2): p. 1679-1696.
• Opara, K.,Arabas, J., Comparison of mutation strategies in differential evolution–a probabilistic perspective, Swarm and Evolutionary Computation, 2018, 39: p. 53-69.
• Singh, A., Laplacian whale optimization algorithm, International Journal of System Assurance Engineering and Management, 2019, 10(4): p. 713-730.
• Li, Y., Zhao, Y., Liu, J., Dimension by dimension dynamic sine cosine algorithm for global optimization problems, Applied Soft Computing, 2021, 98: p. 106933.
• Zhu, A., Xu, C., Li, Z., Wu, J., Liu, Z., Hybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC, Journal of Systems Engineering and Electronics, 2015, 26(2): p. 317-328.
• Gupta, S.,Deep, K., A hybrid self-adaptive sine cosine algorithm with opposition based learning, Expert Systems with Applications, 2019, 119: p. 210-230.
• García, S., Molina, D., Lozano, M., Herrera, F., A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization, Journal of Heuristics, 2009, 15(6): p. 617-644.
• Derrac, J., García, S., Molina, D., Herrera, F., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, 2011, 1(1): p. 3-18.
• Demšar, J., Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research, 2006, 7: p. 1-30.