Document Type : Original Article

Authors

1 Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Management. Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA).
 Methodology: Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.
Findings: The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time".  The results show the excellent performance of the proposed algorithm.
Originality/Value: In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".  

Keywords

Main Subjects

Abualigah, L., Diabat,O A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering376. https://doi.org/10.1016/j.cma.2020.113609
Alam Tabriz, A., Zandieh, M., & Mohammad Rahimi, A. (2008). Meta-heuristic algorithms in hybrid optimization. Ishraqi Publications – Saffar.
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. 2007 IEEE congress on evolutionary computation (pp. 4661-4667). IEEE.
Dehghani, M., Montazeri, Z., Givi, H., Guerrero, J. M., & Dhiman, G. (2020). Darts game optimizer: A new optimization technique based on darts game. Int. J. Intell. Eng. Syst13, 286-294.
Dehghani, M., Montazeri, Z., Malik, O. P., Givi, H., & Guerrero, J. M. (2020). Shell game optimization: a novel game-based algorithm. International journal of intelligent engineering and systems13(3), 246-255.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics, part b (cybernetics)26(1), 29-41.
Eusuff, M. M., & Lansey, K. E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of water resources planning and management129(3), 210-225.
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). Improving the efficiency and effectiveness of the closed loop supply chain: the Sari Wall optimized algorithm approach and new priority-based encryption. Journal of decision making and operations research, 4(4), 299-315. (In Persian). http://www.journal-dmor.ir/article_103943.html
Ghorbani, N., & Babaei, E. (2014). Exchange market algorithm. Applied soft computing19, 177-187.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
Iordache, S. (2010, July). Consultant-guided search: a new metaheuristic for combinatorial optimization problems. Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 225-232). https://doi.org/10.1145/1830483.1830526
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied soft computing32, 72-79.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization39(3), 459-471.
Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: thermal exchange optimization. Advances in engineering software110, 69-84.
Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta mechanica213(3), 267-289.
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks (pp. 1942-1948). IEEE.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science220(4598), 671-680.
Lam, A. Y., & Li, V. O. (2009). Chemical-reaction-inspired metaheuristic for optimization. IEEE transactions on evolutionary computation14(3), 381-399.
Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural computing and applications24(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 applications27(4), 1053-1073.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software95, 51-67.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software69, 46-61.
Mohammad Pour Zarandi, M. A. (2013). Nonlinear optimization. University of Tehran Press.
Molga, M., & Smutnicki, C. (2005). Test functions for optimization needs. Avalable at  http://www.sciepub.com/reference/67412
Osaba, E., Diaz, F., & Onieva, E. (2014). Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied intelligence41(1), 145-166.
Qiao, W., & Yang, Z. (2019). Solving large-scale function optimization problem by using a new metaheuristic algorithm based on quantum dolphin swarm algorithm. IEEE access7, 138972-138989.
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design43(3), 303-315.
Safavi, S. A. A., Pour Jafarian, N., & Safavi, S. A. (2014). Optimization based on meta-heuristic algorithms. Academic Publishing Researchers Publications.
Salimi, H. (2015). Stochastic fractal search: a powerful metaheuristic algorithm. Knowledge-based systems75, 1-18.
Shah-Hosseini, H. (2011). Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimization. International journal of computational science and engineering6(1-2), 132-140.
Sharifzadeh, H., & Amjady, N. (2014). A review of metaheuristic algorithms in optimization. Journal of modeling in engineering, 12(38), 27-43. (In Persian). https://modelling.semnan.ac.ir/article_1677.html?lang=en
Shayeghi, H., & Dadashpour, J. (2012). Anarchic society optimization based PID control of an automatic voltage regulator (AVR) system. Electrical and electronic engineering2(4), 199-207.
Shi, Y. (2015). An optimization algorithm based on brainstorming process. In emerging research on swarm intelligence and algorithm optimization (pp. 1-35). IGI Global.
Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation12(6), 702-713.
Tabari, A., & Ahmad, A. (2017). A new optimization method: Electro-Search algorithm. Computers & chemical engineering103, 1-11.
Wang, T., & Yang, L. (2018). Beetle swarm optimization algorithm: Theory and applicationarXiv preprint arXiv:1808.00206
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation1(1), 67-82.
Yaghini, M., & Akhavan Kazemzadeh, M. R. (2016). Meta-innovation optimization algorithms. Jihad Daneshgahi Publications, Amir Kabir Industrial Branch
Yan, G. W., & Hao, Z. J. (2013). A novel optimization algorithm based on atmosphere clouds model. International journal of computational intelligence and applications12(01), 1350002. https://doi.org/10.1142/S1469026813500028
Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Berlin, Heidelberg: Springer.
Yazdani, M., & Jolai, F. (2016). Lion Optimization Algorithm (LOA): a nature-inspired metaheuristic algorithm. Journal of computational design and engineering3(1), 24-36.
Zhang, L. M., Dahlmann, C., & Zhang, Y. (2009, November). Human-inspired algorithms for continuous function optimization. In 2009 IEEE international conference on intelligent computing and intelligent systems (pp. 318-321). IEEE