Mathematics and Computational Sciences

Competition and cooperation in evading threat meta-heuristic algorithm

Document Type : Original Article

Authors

Mostafa Falahaty nejad 1
Majid Eshaghi gordji 2

1 Department of Mathematics, Semnan University, Semnan, Iran

2 Department of Mathematics, Semnan University, Semnan, Iran,

https://doi.org/10.30511/mcs.2024.2041661.1232
Abstract
This work introduces an innovative heuristic algorithm named "Competition and Collaboration in Evading Threat (CCET)". Inspired by the escape behavior of animals such as deer, buffalo, sheep, etc., from predators like lions, leopards, tigers, etc., and also drawing parallels with soldiers evading attacks in war zones involving missiles, cannons, tanks, enemy gunfire, etc., the algorithm has been devised. In this approach, it is assumed that soldiers in war zones or domesticated animals are fleeing from threats and, despite competing in their escape, they collaborate with each other to ensure their survival. Unlike existing heuristic algorithms that rely on convergence, this proposed algorithm focuses on a novel approach based on the concept of divergence. The optimal response is determined based on the divergence of prey from the threat of the predator. The algorithm undergoes testing on 23 well-known benchmark functions, including unimodal, multimodal, and fixed-dimensional functions. The performance of the proposed algorithm is validated against recognized heuristic algorithms. Comparative results indicate that the proposed algorithm significantly demonstrates the capability to compete with well-known and powerful algorithms.

Keywords

Meta-heuristic algorithm
Optimization
Competition and Cooperation
Divergence
Matlab

Subjects

[1] Abdollahzadeh, B., Gharehchopogh, F. S., Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers Industrial Engineering, 158, 107408.
[2] Adham, M. T., Bentley, P. J. (2014, December). An artificial ecosystem algorithm applied to static and dynamic travelling salesman problems. In 2014 IEEE international conference on evolvable systems (pp. 149-156). IEEE.
[3] Afroughinia, A., Kardehi Moghaddam, R. (2018). Competitive learning: a new meta-heuristic optimization algorithm. International Journal on Artificial Intelligence Tools, 27(08), 1850035.
[4] Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33, 5011-5042.
[5] Atashpaz-Gargari, E., Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667).Ieee.
[6] Brammya, G., Praveena, S., Ninu Preetha, N. S., Ramya, R., Rajakumar, B. R., Binu, D. (2019). Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. The Computer Journal, bxy133.
[7] Civicioglu, P. (2013). Artificial cooperative search algorithm for numerical optimization problems. Information Sciences, 229, 58-76.
[8] Das, S., Chowdhury, A., Abraham, A. (2009, May). A bacterial evolutionary algorithm for automatic data clustering. In 2009 IEEE congress on evolutionary computation (pp. 2403-2410). IEEE.
[9] De Castro, L. N., Von Zuben, F. J. (2000, July). The clonal selection algorithm with engineering applications. In Proceedings of GECCO (Vol. 2000, pp. 36-39).
[10] Digalakis, J. G., Margaritis, K. G. (2001). On benchmarking functions for genetic algorithms. International journal of computer mathematics, 77(4), 481-506.
[11] 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.
[12] Erol, O. K., Eksin, I. (2006). A new optimization method: big bang–big crunch. Advances in engineering software, 37(2), 106-111.
[13] Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers Structures, 110, 151-166.
[14] Halim, A. H., Ismail, I. (2018). Tree physiology optimization in constrained optimization problem. Telkomnika (Telecommunication Computing Electronics and Control), 16(2), 876-882.
[15] Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information sciences, 222, 175-184.
[16] He, S., Wu, Q. H., Saunders, J. R. (2006, July). A novel group search optimizer inspired by animal behavioural ecology. In 2006 IEEE international conference on evolutionary computation (pp. 1272-1278). IEEE.
[17] Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
[18] Karaboga, D., Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39, 459-471.
[19] Karci, A. (2012, September). A new meta-heuristic algorithm based on chemical process: atom algorithm. In 1st International Eurasian Conference on Mathematical Sciences and Applications (pp. 3-7).
[20] Kennedy, J., Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (Vol. 4, pp. 1942-1948). ieee.
[21] Khalid, A. M., Hosny, K. M., Mirjalili, S. (2022). COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle. Neural Computing and Applications, 34(24), 22465-22492.
[22] Kiran, M. S. (2015). TSA: Tree-seed algorithm for continuous optimization. Expert Systems with Applications, 42(19), 6686-6698.
[23] Kirkpatrick, S. (1983). Improvement of reliabilities of regulations using a hierarchical structure in a genetic network. Science, 220, 671-680.
[24] Koza, J. R. (1990, November). Genetically breeding populations of computer programs to solve problems in artificial intelligence. In [1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence (pp. 819-827). IEEE.
[25] Marinakis, Y., Marinaki, M., Matsatsinis, N. (2010). A bumble bees mating optimization algorithm for global unconstrained optimization problems. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 305-318). Berlin, Heidelberg: Springer Berlin Heidelberg.
[26] Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., Zhang, H. (2016). A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. Journal of Experimental Theoretical Artificial Intelligence, 28(4), 673-687.
[27] Mirjalili, S., Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm and Evolutionary Computation, 9, 1-14.
[28] Mirjalili, S., Mirjalili, S. M., Yang, X. S. (2014). Binary bat algorithm. Neural Computing and Applications, 25, 663-681.
[29] Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
[30] Mirjalili, S. (2015). The ant lion optimizer. Advances in engineering software, 83, 80-98.
[31] Mirjalili, S., Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
[32] Molga, M., Smutnicki, C. (2005). Test functions for optimization needs. Test functions for optimization needs, 101, 48.
[33] Mucherino, A., Seref, O. (2007, November). Monkey search: a novel metaheuristic search for global optimization. In AIP conference proceedings (Vol. 953, No. 1, pp. 162-173). American Institute of Physics.
[34] Odili, J. B., Kahar, M. N. M., Anwar, S. (2015). African buffalo optimization: a swarm-intelligence technique. Procedia Computer Science, 76, 443-448.
[35] Oftadeh, R., Mahjoob, M. J. (2009, September). A new meta-heuristic optimization algorithm: Hunting Search. In 2009 fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control (pp. 1-5). IEEE.
[36] 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, 145-166.
[37] Price, K. V., Storn, R. M., Lampinen, J. A. (2005). The differential evolution algorithm. Differential evolution: a parctical approach to global optimization, 37-134.
[38] Purnomo, H. D., Wee, H. M. (2013). Soccer game optimization: an innovative integration of evolutionary algorithm and swarm intelligence algorithm. In Meta-Heuristics optimization algorithms in engineering, business, economics, and finance (pp. 386-420). Igi Global.
[39] Rabanal, P., Rodriguez, I., Rubio, F. (2007, August). Using river formation dynamics to design heuristic algorithms. In International conference on unconventional computation (pp. 163-177). Berlin, Heidelberg: Springer Berlin Heidelberg.
[40] Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied soft computing, 11(8), 5508-5518.
[41] Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S. (2009). GSA: a gravitational search algorithm. Information sciences, 179(13), 2232-2248.
[42] Sayed, G. I., Tharwat, A., Hassanien, A. E. (2019). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49, 188-205.
[43] Sebald, A. V., Fogel, L. J. (1994). Evolutionary Programming: Proceedings of the Third Annual Conference. In Evolutionary Programming: Proceedings of the Third Annual Conference (pp. 1-386).
Mathematics and Computational Sciences
Volume 6, Issue 1
Winter 2025
Pages 44-66

  • Receive Date 21 September 2024
  • Revise Date 26 November 2024
  • Accept Date 13 December 2024

Home

Submit Manuscript

Reviewers

Contact Us