Global Optimizasyonu için Uygunluk Mesafe Dengesi Tabanlı Rehber Mekanızmasıyla Slime Mould Optimize Edicinin İyileştirilmesi

Year 2021, , 40 - 54, 31.12.2021

Çağrı Suiçmez , Hamdi Kahraman , Cemal Yılmaz , Mehmet Fatih Işık , Enes Cengiz

https://doi.org/10.29130/dubited.1016209

Cited By: 5

Abstract

Bu çalışmada güncel bir Meta-Heuristic Search algoritması olan Balçık-Küfü Algoritması (SMA) performansının iyileştirmesi yapılmaktadır. SMA algoritmasında arama süreci yaşam döngüsü sürecini daha etkili bir şekilde modelleyebilmek için arama sürecine rehberlik eden çözüm adayları uzaklık-uygunluk dengesi (fitness-distance balance, FDB) yöntemi kullanılarak belirlenmiştir. Her ne kadar SMA algoritmasının performansı kabul görse de uygulanan FDB yöntemi sayesinde geliştirilen FDB-SMA algoritmasının performansının çok daha iyi olduğu görülmektedir. Geliştirilen FDB-SMA algoritmasının performansını test etmek için güncel benchmark sorunları olan CEC 2020 kullanılmıştır. CEC 2020'den alınan 10 farklı kısıtsız kıyaslama problemi 30-50-100 boyutlarında düzenlenerek tasarlanmıştır. Deneysel çalışmalar tasarlanan kıyaslama problemleri kullanılarak gerçekleştirilmiş ve Friedman ve Wilcoxon istatistiksel test yöntemleri ile analiz edilmiştir. Analiz sonuçlarına göre FDB-SMA varyasyonlarının tüm deneysel çalışmalarda temel algoritmaya (SMA) göre daha üstün bir performans gösterdiği görülmüştür.

Keywords

Meta-sezgisel arama, Slime Mould algoritması, uygunluk-mesafe dengesi (FDB), kıyaslama problemleri

Improved Slime-Mould-Algorithm with Fitness Distance Balance-based Guiding Mechanism for Global Optimization Problems

Abstract

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.

Keywords

Meta-heuristic search, slime mould algorithm, fitness-distance balance (FDB), benchmark problems

References

• [1] A. Kaveh and S. Talatahari, “An improved ant colony optimization for constrained engineering design problems,” Engineering Computations, vol. 27, no. 1, pp. 155-182, 2010.
• [2] A. H. Halim, I. Ismail, and S. Das, “Performance assessment of the metaheuristic optimization algorithms: an exhaustive review,” Artificial Intelligence Review, vol. 54, no. 3, pp. 2323-2409, 2020.
• [3] H. Chen, S. Jiao, A. A. Heidari, M. Wang, X. Chen and X. Zhao, “An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models,” Energy Convers. Manage., vol. 195, pp. 927–942, 2019.
• [4] H. Chen, Y. Xu, M. Wang and X. Zhao, “A balanced whale optimization algorithm for constrained engineering design problems,” Appl. Math. Model., vol. 71, pp. 45–59, 2019.
• [5] M. Wang, H. Chen, “Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis,” Appl. Soft Comput., vol. 88, 2020.
• [6] A. Kaveh and S. Talatahari, “A novel heuristic optimization method: charged system search,” Acta Mechanica, vol. 213, no. 3, pp. 267-289, 2010.
• [7] L. J. Fogel, A. J. Owens, and M. J. Walsh, “Intelligent decision making through a simulation of evolution,” Behavioral Science, vol. 11, no. 4, pp. 253-272, 1966.
• [8] D. E. Goldberg, and J. H. Holland, “Genetic Algorithms and Machine Learning,” Machine Learning, vol. 3, no. 2, pp. 95-99, 1988.
• [9] F. Glover, “Heuristics for integer programming using surrogate constraints,” Decision Sciences, vol. 8, no. 1, pp.156-166, 1977.
• [10] M. Drigo, “he Ant System: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics-Part B, vol. 26, no. 1, pp. 1-13, 1996.
• [11] R. Eberhart, and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43.
• [12] D. Bertsimas, and J. Tsitsiklis, “Simulated annealing,” Statistical Science, vol. 8, no. 1, pp.10-15, 1993.
• [13] A. Franzin and T. Stützle, "Revisiting simulated annealing: A component-based analysis," Computers & Operations Research, vol. 104, pp. 191-206, 2019.
• [14] O. K. Erol, and I. Eksin, “A new optimization method: big bang–big crunch,” Advances in Engineering Software, vol. 37, no. 2, pp. 106-111, 2006.
• [15] A. Kaveh and S. Talatahari, “Size optimization of space trusses using Big Bang–Big Crunch algorithm,” Computers & Structures, vol. 87, no. 17-18, pp. 1129-1140, 2009.
• [16] E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
• [17] Z. W. Geem, J. H. Kim, and G. V. Loganathan. “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2 pp. 60-68, 2001.
• [18] D. Karaboga, and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
• [19] H. T. Kahraman, S. Aras, and E. Gedikli, “Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms,” Knowledge-Based Systems, vol. 190, 2020.
• [20] R. Salgotra, U. Singh, and S. Saha, “New cuckoo search algorithms with enhanced exploration and exploitation properties,” Expert Systems with Applications, vol. 95, pp. 384-420, 2018.
• [21] S. Aras, E. Gedikli, and H. T. Kahraman, “A novel stochastic fractal search algorithm with fitness-Distance balance for global numerical optimization,” Swarm and Evolutionary Computation, vol. 61, 2021.
• [22] S. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili, “Slime mould algorithm: A new method for stochastic optimization,” Future Generation Computer Systems, vol. 111, pp. 300-323, 2020.
• [23] D. H. Wolpert, and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no .1, pp. 67-82, 1997.
• [24] X. S. Yang, Nature-inspired Metaheuristic Algorithms, 2nd ed., vol. 1, Cambridge, UK: Luniver Press, 2010, pp. 1-11
• [25] M. Katı ve H. T. Kahraman, “Arz-talep tabanlı optimizasyon algoritmasinın fdb yöntemi ile iyileştirilmesi: mühendislik tasarım problemleri üzerine kapsamlı bir araştırma,” Mühendislik Bilimleri ve Tasarım Dergisi, c. 8, s. 5, ss. 156-172, 2020.
• [26] T. Schmickl, and K. Crailsheim, “A navigation algorithm for swarm robotics inspired by slime mold aggregation,” in International Workshop on Swarm Robotics, Rome, Italy, 2006, pp.1-13.
• [27] A. Brabazon, and S. McGarraghy, “Slime mould foraging: an inspiration for algorithmic design,” International Journal of Innovative Computing and Applications, vol. 11, no. 1, pp. 30-45, 2020.
• [28] F. L. Howard, “The life history of Physarum polycephalum.” American Journal of Botany, vol. 18, no. 2, pp. 116-133, 1931.
• [29] M. Becker, “On the efficiency of nature-inspired algorithms for generation of fault-tolerant graphs,” in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 1657-1663.
• [30] V. ŠešumČavić, E. Kühn and D. Kanev, “Bio-inspired search algorithms for unstructured P2P overlay networks,” Swarm Evol. Comput, vol. 29, pp. 73–93, 2016.
• [31] M. Beekman and Tanya Latty, “Brainless but multi-headed: decision making by the acellular slime mould Physarum polycephalum,” Journal of Molecular Biology, vol. 427, no. 23, pp. 3734-3743, 2015.
• [32] T. Latty, and M. Beekman, “Speed–accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum,” Proceedings of The Royal Society B: Biological Sciences, vol. 278, no. 1705, pp. 539-545, 2011.
• [33] P. Kareiva and G. Odell, “Swarms of predators exhibit” preytaxis “if individual predators use area-restricted search,” The American Naturalist, vol. 130, no. 2, pp. 233-270, 1987.
• [34] T. Latty and M. Beekman, “Food quality affects search strategy in the acellular slime mould, Physarum polycephalum,” Behavioral Ecology, vol. 20, no. 6, pp. 1160-1167, 2009.
• [35] A. P. Piotrowski and J. J. Napiorkowski, “Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure?,” Swarm And Evolutionary Computation, vol. 43, pp. 88-108, 2018.
• [36] A. W. Mohamed, A. A. Hadi and K. M. Jambi, “Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization,” Swarm and Evolutionary Computation, vol. 50, pp. 1-14, 2019.
• [37] N. Di Cesare and M. Domaszewski, “A new hybrid topology optimization method based on I-PR-PSO and ESO. Application to continuum structural mechanics,” Computers & Structures, vol. 212, pp. 311-326, 2019.
• [38] S. Torabi and F. Safi-Esfahani, “Improved raven roosting optimization algorithm (IRRO),” Swarm and Evolutionary Computation, vol. 40, pp. 144-154, 2018.
• [39] R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Transactions on Cybernetics, vol. 45, no. 2, pp. 191-204, 2014.
• [40] X. Han, Q. Liu, H. Wang and L. Wang, “Novel fruit fly optimization algorithm with trend search and co-evolution,” Knowledge-Based Systems, vol. 141, pp. 1-17, 2018.
• [41] U. Guvenc, S. Duman, H. T. Kahraman, S. Aras and M. Katı, “Fitness–Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources,” Applied Soft Computing, vol. 108, pp. 1-35, 2021.
• [42] H.T. Kahraman, H. Bakir, S. Duman, M. Katı, S. Aras and U. Guvenc, “Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination,” Applied Intelligence, pp. 1-36, 2021
• [43] T. Kadavy, M. Pluhacek, A. Viktorin, and R. Senkerik, “SOMA-CL for competition on single objective bound constrained numerical optimization benchmark: a competition entry on single objective bound constrained numerical optimization at the genetic and evolutionary computation conference (GECCO) 2020,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020, pp. 9-10.