مقایسه و رتبهبندی الگوریتمهای فراابتکاری با استفاده از روشهای تصمیمگیری گروهی
الموضوعات :Hojatollah Rajabi Moshtaghi 1 , Abbas Toloie Eshlaghy 2 , Mohammad Reza Motadel 3
1 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: روشهای تصمیمگیری گروهی, رتبهبندی الگوریتمهای فراابتکاری, الگوریتمهای ازدحامی و تکاملی, الگوریتمهای فراابتکاری,
ملخص المقالة :
در سالهای اخیر، شاهد ظهور و گسترش الگوریتمهای فراابتکاری و استفاده از آنها جهت حل مسائل پیچیده، غیرخطی و NP-hard بودهایم. هدف از انجام این تحقیق رتبهبندی الگوریتمهای فراابتکاری با استفاده از روشهای تصمیمگیری گروهی بوده است. در این راستا، پنج الگوریتم شامل: GA، PSO، ABC،SFLA و ICA انتخاب و با بهرهگیری از 15 تابع تست استاندارد و همچنین با در نظر گرفتن دو شاخص میانگین تابع هدف و میانگین زمان محاسباتی مقایسهها انجام شد. در ادامه الگوریتم ها بوسیله سه تکنیک تصمیمگیری گروهی شامل:کوک وسیفرد، کندرست و دادسون رتبهبندی گردیدند. علاوه بر این، در این پژوهش برای خروج از گره حاصل از یکسان شدن رتبه برخی از گزینهها در روشهای کندرست و دادسون راه حلهایی پیشنهاد و سپس الگوریتمهای تحت بررسی، با روشهای پیشنهادی نیز رتبهبندی شدند. در نهایت رتبهبندی کلی با استفاده از یک مدل تخصیص انجام شد، که نتایج آن به صورت زیر است: رتبه یکم PSO ، رتبه دوم ICA ، رتبه سوم GA، رتبه چهارم ABC و رتبه پنجم SFLA .
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- Dehghani, M., Montazeri, Z., Givi, H., Guerrero, J. M., & Dhiman, G. (2020). Darts game optimizer: a new optimization technique based on darts game. J. Intell. Eng. Syst, 13(1), 286-294.
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- Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Tavakkoli-Moghaddam, R. (2020). Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft computing, 19(1), 1-29.
- Ghahramani Nahr, J. (2019). Improve the efficiency and effectiveness of the closed loop supply chain: Wall optimization algorithm and new coding based on priority approach. Decisions and operations research, 4(4), 299-315. (In Persian).
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- Karaboga, D., & Basturk. B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
- Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system Search. Acta Mechanica, 213(3-4), 267-289.
- Li, X. X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, 24(7), 1867–1877.
- Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.
- Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.
- Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
- Mohammad Pour Zarandi, M. E. (2013). Nonlinear optimization. Tehran, Tehran University press. (In Persian).
- Molga, M., & Smutnicki, C. (2005). Test functions for optimization needs. Test functions for optimization needs, 101, p. 48.
- Osaba, E., Diaz, F., & Onieva, E. (2014). Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, 41(1), 145–166.
- Raouf, O. A., & Hezam, I. M. (2017). Sperm motility algorithm: a novel metaheuristic approach for global optimization. International Journal of Operational Research (IJOR), 28(2), 43-63.
- Salimi, H. (2015). Stochastic Fractal Search: A powerful metaheuristic algorithm. Knowledge-Based Systems, 75, 1-18.
- Sharifzadeh, H., & Amjady, N. (2014). A Review of metaheuristic algorithms in Journal of modeling in engineering, 12(38), 27-43. (In Persian). DOI: 10.22075/jme.2017.1677.
- Tabari, A., & Arshad, A. (2017). A new optimization method: Electro-Search algorithm. Computers and Chemical Engineering, 103, 1–11.
- Wang, T., & Yang, L. (2018). Beetle swarm optimization algorithm: Theory and application. ArXiv: 1808.00206v2.
- Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
- Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Proceedings of the Fourth International Workshop on Nature inspired cooperative strategies for optimization (NICSO 2010), Berlin, Heidelberg, 65-74.
- Yazdani, M., & Jolai, F. (2016). Lion Optimization Algorithm (LOA): A nature-inspired meta-heuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24-36.