Yılan Optimizasyon Algoritması ile CEC 2019 Problem Seti ve Mühendislik Problemlerinin Çözümü

Year 2024, Volume: 3 Issue: 2, 112 - 122, 25.11.2024

Merve Arslan , Gurcan Yavuz

Abstract

Optimizasyon problemlerinin çözümünde Meta-sezgisel algoritmaların kullanımı yaygındır. Bu çalışmada, CEC-2019 problem setinin çözümünde Yılan Optimizasyonu (Snake Optimization, SO) Algoritması kullanılmaktadır. Elde edilen sonuçlar literatürde sık kullanılan, güncel ve başarılı sonuçlar elde edilmiş diğer optimizasyon algoritmalarından Koati Optimizasyon Algoritması (Coati Optimization Algorithm, COA), Bernstein Arama DE Algoritması (Bernstein-Search Differential Evolution, BSDE), Karşıt-Karşılıklı Öğrenme DE (Oppositional-Mutual Learning Differential Evolution, OMLDE), Ağırlıklı Diferansiyel Evrim (Weighted Differential Evolution, WDE), Parçacık Sürü Optimizasyonu (Particle Swarm Optimization, PSO), Başarı Geçmişi Uyarlaması DE (Success-History Adaptation Differential Evolution, SHADE), İsteğe Bağlı Harici Arşivle Uyarlanabilir DE (Adaptive Differential Evolution with Optional External Archive, JADE), Kovaryans Matrisi Uyarlama Evrim Stratejisi (Covariance Matrix Adaptation Evolution Strategy, CMAES) ve Bernstein Operatörü ve Kırılmış Karşıt Karşılıklı Öğrenme Diferansiyel Evrim (Bernstein Operator and Refracted Oppositional Mutual Learning Differential Evolution, BROMLDE) ile karşılaştırılmaktadır. İlklendirme aşamasında, çözüm uzayını daha iyi keşfetmesi adına ve optimum çözüme yakınsamasının hızlandırılması açısından Latin hiperküp tekniği kullanılmaktadır. Geliştirilen yılan algoritmasının performansı CEC 2019 problem çözümünde değerlendirilmektedir. Sonuçları analiz etmek için Friedman testi kullanılmaktadır. Elde edilen sonuçlara göre Yılan algoritmasının 10 problemden 6’sında rakiplerinden daha iyi sonuçlar verdiği görülmektedir. Ek olarak Yılan Optimizasyonu ile iki adet mühendislik problemi çözümü yapılmaktadır. Mühendislik problemi çözüm sonuçları literatürde bulunan diğer optimizasyon algoritmaları sonuçları ile kıyaslanmaktadır. Sonuç olarak Yılan Algoritması diğer optimizasyon algoritmaları ile karşılaştırıldığında rekabetçi sonuçlar vermektedir.

Keywords

Yılan Optimizasyon Algoritması, Latin Hiperküp Örnekleme, CEC 2019, Dişli Sistemi Tasarımı Problemi, Borulu Kolon Tasarımı

Solving of CEC 2019 Problem Set and Engineering Problems with the Snake Optimization Algorithm

Abstract

Meta-heuristic algorithms are commonly used in solving optimization problems. In this study, the Snake Optimization (SO) Algorithm is employed to solve the CEC-2019 problem set. The obtained results are compared with other optimization algorithms that are frequently used in the literature and have achieved recent successful outcomes, including the Coati Optimization Algorithm (COA), Bernstein-Search Differential Evolution (BSDE), Oppositional-Mutual Learning Differential Evolution (OMLDE), Weighted Differential Evolution (WDE), Particle Swarm Optimization (PSO), Success-History Adaptation Differential Evolution (SHADE), Adaptive Differential Evolution with Optional External Archive (JADE), Covariance Matrix Adaptation Evolution Strategy (CMAES), and Bernstein Operator and Refracted Oppositional Mutual Learning Differential Evolution (BROMLDE). In the initialization phase, the Latin hypercube technique is used to explore the solution space more effectively and accelerate convergence to the optimal solution. The performance of the developed Snake Algorithm is evaluated in solving the CEC 2019 problem set. The Friedman test is used to analyze the results. According to the results, the Snake Algorithm outperforms its competitors in 6 out of 10 problems. Additionally, two engineering problems are solved using the Snake Optimization algorithm. The results of the engineering problem solutions are compared with the results of other optimization algorithms in the literature. In conclusion, the Snake Algorithm provides competitive results when compared to other optimization algorithms.

Keywords

Snake Optimization Algorithm, Latin Hypercube Sampling, CEC 2019, Gear Train Design Problem, Tubular Column Design Problem

References

• J. H. Holland, “Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. Ann Arbor, MI: University of Michigan Press, 1975.
• J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of the IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.
• R. Storn and K. Price, “Differential evolution-a simple and efficient heuristic for global optimization over continuous space,” Journal of Global Optimization, vol. 11, pp. 341–359, 1997.
• Y. Çelik, İ. Yıldız, and A. T. Karadeniz, “A brief review of metaheuristic algorithms improved in the last three years,” Avrupa Bilim ve Teknoloji Dergisi, pp. 463–477, 2019.
• M. Dorigo, V. Maniezzo, and A. Colorni, “Positive feedback as a search strategy,” pp. 91–107, 1991.
• S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
• X.-S. Yang, Nature-inspired metaheuristic algorithms: Second edition, 2nd ed. Frome, England: Luniver Press, 2010.
• F. A. Hashim and A. G. Hussien, “Snake optimizer: A novel meta-heuristic optimization algorithm,” Knowledge Based System, vol. 242, no. 108320, p. 108320, 2022.
• A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, and A. Zamuda, “DISH algorithm solving the CEC 2019 100-digit challenge,” in 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, 2019, pp. 1-6.
• Vigya, S. Raj, C. K. Shiva, B. Vedik, S. Mahapatra, and V. Mukherjee, “A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem,” Chaos Solitons Fractals, vol. 173, no. 113672, p. 113672, 2023.
• Y. Duan and X. Yu, “A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems,” Expert System with Application, vol. 213, no. 119017, p. 119017, 2023.
• B. Durmuş, “Kaotik harita temelli ağaç tohum algoritması,” Süleyman demirel üniversitesi fen bilimleri enstitüsü dergisi, c. 23, sy. 2, ss. 601–610, 2019.
• S. Barua and A. Merabet, “Lévy arithmetic algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization,” Expert Syst. Appl., vol. 241, no. 122335, p. 122335, 2024.
• D. Pelusi, R. Mascella, L. Tallini, J. Nayak, B. Naik, and Y. Deng, “An Improved Moth-Flame Optimization algorithm with hybrid search phase,” Knowl. Based Syst., vol. 191, no. 105277, p. 105277, 2020.
• T. Thaher, A. Sheta, M. Awad, and M. Aldasht, “Enhanced variants of crow search algorithm boosted with cooperative based island model for global optimization,” Expert Syst. Appl., vol. 238, no. 121712, p. 121712, 2024.
• O. J. Agushaka and A. E.-S. Ezugwu, “Influence of initializing krill herd algorithm with low-discrepancy sequences,” IEEE Access, vol. 8, pp. 210886–210909, 2020.
• D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.
• K. V. Price, N. H. Awad, M. Z. Ali, and P. N. Suganthan, “Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization”, Technical report. Singapore: Nanyang Technological University, 2018.
• M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” Ann. Math. Stat., vol. 11, no. 1, pp. 86–92, 1940.
• M. D. Mckay, R. J. Beckman, and W. J. Conover, “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics, vol. 42, no. 1, pp. 55–61, 2000.
• F. Wu, J. Zhang, S. Li, D. Lv, and M. Li, “An enhanced differential evolution algorithm with Bernstein operator and refracted oppositional-mutual learning strategy,” Entropy (Basel), vol. 24, no. 9, p. 1205, 2022.
• P. Civicioglu and E. Besdok, “Bernstain-search differential evolution algorithm for numerical function optimization,” Expert System with Applications., vol. 138, no. 112831, p. 112831, 2019.
• Y. Xu et al., “An enhanced differential evolution algorithm with a new oppositional-mutual learning strategy,” Neurocomputing, vol. 435, pp. 162–175, 2021.
• P. Civicioglu, E. Besdok, M. A. Gunen, and U. H. Atasever, “Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms,” Neural Computing and Applications, vol. 32, no. 8, pp. 3923–3937, 2020.
• 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, Nagoya, Japan, 1995, pp. 39-43.
• R. Tanabe and A. Fukunaga, “Success-history based parameter adaptation for Differential Evolution,” in 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, 2013, pp. 71-78.
• J. Q. Zhang and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive”, IEEE Transactions Evolutionary Computation, vol. 13, pp. 945–958, 2009.
• N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evol. Comput., vol. 9, no. 2, pp. 159–195, 2001.
• E. Sandgren, “Nonlinear integer and discrete programming inmechanical design,” in Proceeding of the ASME design technology conference, 1998.
• S. S. Rao, Engineering Optimization: Theory and Practice. Hoboken, NJ, USA: Wiley, 2009.
• S. Arora, S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft Comput., vol. 23, no. 3, pp. 715–734, 2019.
• I. Naruei, F.Keynia, “A new optimization method based on COOT bird natural life model,” Expert Syst. Appl. Vol. 183, no. 115352, p.115352, 2021.