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  • مقایسه و رتبه‌بندی الگوریتم‌های فراابتکاری با استفاده از روش‌های تصمیم‌گیری گروهی

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آدرس مقاله


کد مقاله : IMJ-2103-1578 (R1) بازدید : 115 صفحه: 65 - 79

10.30495/imj.2022.688625

نوع مقاله: پژوهشی

مقایسه و رتبه‌بندی الگوریتم‌های فراابتکاری با استفاده از روش‌های تصمیم‌گیری گروهی

محورهای موضوعی : مدیریت صنعتی

Hojatollah Rajabi Moshtaghi 1 (Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Abbas Toloie Eshlaghy 2 (Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Mohammad Reza Motadel 3 (Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)

تاریخ دریافت : 1400/01/05 تاریخ پذیرش : 1400/07/24 تاریخ انتشار : 1400/11/01

کلید واژه: روش‌های تصمیم‌گیری گروهی, رتبه‌بندی الگوریتم‌های فراابتکاری, الگوریتم‌های ازدحامی و تکاملی, الگوریتم‌های فراابتکاری,

چکیده مقاله :

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 .

چکیده انگلیسی:

In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear and NP-hard problems have dramatically increased, while new algorithms have constantly being introduced. In this research, with the aim of ranking meta-heuristic algorithms, using group decision making techniques (different from other research in this field), 5 algorithms including: GA, PSO, ABC, SFLA and ICA by 15 standard test functions, and considering 2 attribute: "mean of answers" and "run time", have been compared. Then they are ranked by 3 group decision making methods including: "Cook and Seiford", "Condorcet" and "Dodgson". In addition, as in ranking by "Condorcet" and "Dodgson" methods, sometimes some options posit the same rank, therefore, in this study; we presented a proposal to overcome the limitation. Then the algorithms with these proposed methods were ranked. Finally, the overall ranking is done using an allocation model our results show that the overall ranking is as follows, respectively: PSO, ICA, GA, ABC and SFLA.

منابع و مأخذ:


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_||_

  1. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021).The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609.
    2.
  2. Alam Tabriz, A., Zandieh, M., & Mohammad Rahimi, A. (2013). Meta-heuristic algorithms in hybrid optimization. Saffar-Eshraiggi Press. (In Persian).
    3.
  3. Asgharpour, M. J. (2014). Group Decision Making and Game Theory in Operation Research. Tehran, Tehran University press. (In Persian).
    4.
  4. 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.
    5.
  5. Eshghi, K., & Karimi-Nasab, M. (2016).Analysis of algorithms and Design of metaheuristic Tehran, Sharif University Press. (In Persian).
    6.
  6. 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.
    7.
  7. 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).
    8.
  8. Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied Soft Computing, 32, 72-79.
    9.
  9. 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.
    10.
  10. Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system Search. Acta Mechanica, 213(3-4), 267-289.
    11.
  11. 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.
    12.
  12. 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.
    13.
  13. Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.
    14.
  14. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
    15.
  15. Mohammad Pour Zarandi, M. E. (2013). Nonlinear optimization. Tehran, Tehran University press. (In Persian).
    16.
  16. Molga, M., & Smutnicki, C. (2005). Test functions for optimization needs. Test functions for optimization needs, 101, p. 48.
    17.
  17. 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.
    18.
  18. 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.
    19.
  19. Salimi, H. (2015). Stochastic Fractal Search: A powerful metaheuristic algorithm. Knowledge-Based Systems, 75, 1-18.
    20.
  20. 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.
    21.
  21. Tabari, A., & Arshad, A. (2017). A new optimization method: Electro-Search algorithm. Computers and Chemical Engineering, 103, 1–11.
    22.
  22. Wang, T., & Yang, L. (2018). Beetle swarm optimization algorithm: Theory and application.  ArXiv: 1808.00206v2.
    23.
  23. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
    24.
  24. 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.
    25.
  25. 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.
    26.

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