MARTI OPTİMİZASYON ALGORİTMASININ KISITLI MÜHENDİSLİK TASARIM PROBLEMLERİ İÇİN PERFORMANS ANALİZİ

Yıl 2021, Cilt: 8 Sayı: 15, 469 - 485, 31.12.2021

Feyza Altunbey Özbay , Erdal Özbay

https://doi.org/10.54365/adyumbd.990708

Cited By: 1

Öz

Metasezgisel arama algoritmaları, birçok uygulama alanında farklı optimizasyon problemlerini çözmek için yaygın bir biçimde kullanılmaktadır. Özellikle, son yıllarda, karmaşık optimizasyon problemlerini etkin bir biçimde çözebilmek için fiziksel, kimyasal veya biyolojik olaylardan esinlenilerek birçok farklı metasezgisel algoritma geliştirilmiştir. Doğadaki martıların göç ve saldırı davranışlarından ilham alınarak geliştirilen Martı Optimizasyon Algoritması (MOA), maliyetli optimizasyon problemlerinin çözümü için etkili biyoloji tabanlı metasezgisel bir yöntemdir. Bu çalışmada, MOA’nın performansını değerlendirmek için, MOA amaç fonksiyonları, kısıtları ve karar değişkenleri farklı beş kısıtlı mühendislik tasarım problemine uygulanmıştır. MOA ile elde edilen sonuçlar, farklı metasezgisel algoritmalar ile karşılaştırılmıştır. Elde edilen deney sonuçlarına göre, MOA, karşılaştırılan diğer optimizasyon yöntemlerine göre oldukça iyi sonuçlar vermiştir.

Anahtar Kelimeler

Martı optimizasyon algoritması, Metasezgisel algoritmalar, Mühendislik tasarım problemi

Kaynakça

• Altunbey F, Alataş B. Review of social-based artificial ıntelligence optimization algorithms for social network analysis. International Journal of Pure and Applied Sciences 2015; 1(1): 33-52.
• Erol OK, Eksin I. A new optimization method: big bang–big crunch. Advances in Engineering Software 2006; 37(2): 106-111.
• Hatamlou A. Black hole: A new heuristic optimization approach for data clustering. Information Sciences 2013; 222: 175-184.
• Formato RA. Central force optimization: A new deterministic gradient like optimization metaheuristic. Opsearch 2009; 46(1): 25–51.
• Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Information Sciences 2009; 179(13): 2232-2248.
• Eskandar H, Sadollah A, Bahreininejad A, Hamdi M. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures 2012; 110: 151-166.
• Kennedy J, Eberhart R. Particle swarm optimization. Paper presented at: ICNN’95-IEEE International Conference on Neural Networks, 1995.
• Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE Computational İntelligence Magazine 2006; 1(4): 28-39.
• Yang XS. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies For Optimization (NICSO 2010), Springer, Berlin, Heidelberg. 2010: 65-74.
• Yang XS. Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation 2010; 2(2):78–84.
• Karaboga D, Basturk, BA powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization 2007; 39(3): 459-471.
• Dhiman G, Singh KK, Soni M, Nagar A, Dehghani M, Slowik A, Cengiz K. MOSOA: a new multi-objective seagull optimization algorithm. Expert Systems with Applications 2021; 167, 114150.
• Dhiman G, Kumar V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems 2019; 165: 169-196.
• Xu L, Mo Y, Lu Y, Li J. Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems. Processes 2021; 9(6): 1037.
• Del Hoyo J, Elliott A, Sargatal J. Handbook of the Birds of the World. Barcelona: Lynx edicions 1992: 1(8),
• Macdonald SM, Mason CF. Predation of migrant birds by gulls. British Birds 1973; 66: 361-363.
• Dhiman G, Kumar V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems 2019; 165: 169-196.
• Coello CAC. Use of a self-adaptive penalty approach for engineering optimization problems, Computers in Industry 2000; 41: 113–127.
• Askarzadeh A. A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & Structures 2016; 169: 1-12.
• He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering applications of artificial intelligence 2007; 20(1): 89-99.
• Sadollah A, Bahreininejad A, Eskandar H, Hamdi M. Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing 2013; 13(5): 2592-2612.
• He Q, Wang L. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation 2007; 86: 1407–1422.
• Huang FZ, Wang L, He Q. An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and computation 2007; 186(1): 340-356.
• Akay B, Karaboga, D. Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of intelligent manufacturing 2012; 23(4): 1001-1014.
• Mirjalili S. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems 2016; 96: 120-133.
• Mirjalili S, Mirjalili SM, Hatamlou A. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications 2016; 27(2): 495-513.
• Arora JS. Introduction to Optimum Design. McGraw-Hill, New York, 1989.
• Kannan BK, Kramer SN. An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. Journal of Mechanical Design 1994; 16: 405–411.
• Gandomi AH, Yang XS. (2011). Benchmark problems in structural optimization. In Computational optimization, methods and algorithms, Springer, Berlin, Heidelberg; 2011: 259-281.
• Mezura-Montes E, Coello CAC. (2005, November). Useful infeasible solutions in engineering optimization with evolutionary algorithms. In Mexican international conference on artificial intelligence, Springer, Berlin, Heidelberg 2005: 652-662.
• Mezura-Montes E, Velázquez-Reyes J, Coello CC. Modified differential evolution for constrained optimization. Paper presented at: 2006 IEEE International Conference on Evolutionary Computation 2006: 25-32.
• Ray T, Liew KM. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation 2003; 7(4): 386-396.
• Eskandar H, Sadollah A, Bahreininejad A, Hamdi M. Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures 2012; 110: 151-166.
• Wang Y, Cai Z, Zhou Y, Fan Z. (2009). Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Structural and Multidisciplinary Optimization 2009; 37(4): 395-413.
• Fujita Y, Lind K, Williams TJ. (1974). Computer applications in the automation of shipyard operation and ship design. Paper presented at: IFIP/IFAC/JSNA joint conference 1973; 2: 28-30.