Impact of popular chaotic maps on population initialization of Sailfish Optimizer Algorithm – An experimental study
Abstract
Initial population in a swarm intelligence-based optimization algorithm plays an important role in avoiding local optima, early convergence, and global space exploration. If initial values provide proper coverage of the search space, then it may quickly lead to the optimal solution, whereas limited coverage may stick an algorithm into local optima. Several techniques are presented by researchers to properly initialize the population instead of random numbers. Chaos theory is a well-known concept utilized in designing an improved variant of various existing nature-inspired optimization algorithms. However, its impact on the initial population of a metaheuristic was never thoroughly studied before. This study investigates the impact of ten chaotic maps on the initial population of a sailfish optimizer algorithm (SFO). SFO is a recent metaheuristic algorithm which mimics the hunting behavior of sailfish. Ten chaotic maps have been used to initialize sailfish and sardine populations. All techniques (with or without chaotic maps) are tested with twenty-three classical benchmark functions. The numerical results, ranking and statistical analysis show that chaotic maps are useful in avoiding local optima and are effective for improving the global search capabilities of a metaheuristic.
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Hefny, H. A., & Azab, S. S. (2010). Chaotic particle swarm optimization. 2010 The 7th International Conference on Informatics and Systems (INFOS), 1–8.
Huang, X., Zeng, X., Han, R., & Wang, X. (2019). An enhanced hybridized artificial bee colony algorithm for optimization problems. IAES International Journal of Artificial Intelligence, 8(1). https://doi.org/10.11591/ijai.v8.i1.pp87-94
Javidi, M., & Hosseinpourfard, R. (2015). Chaos genetic algorithm instead genetic algorithm. International Arab Journal of Information Technology, 12(2).
Jianhao, W., Long, W., Lijie, C., & Tian, G. (2021). Enhanced whale optimization algorithm for large-scale global optimization problems. 2021 International Conference on Computer Communication and Artificial Intelligence, CCAI 2021, 180–187. https://doi.org/10.1109/CCAI50917.2021.9447541
Jin, Q., Xu, Z., & Cai, W. (2021). An improved whale optimization algorithm with random evolution and special reinforcement dual‐operation strategy collaboration. Symmetry, 13(2), 1–24. https://doi.org/10.3390/sym13020238
Karaboga, D., & Basturk, B. (2007). Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. Lecture Notes in Computer Science, 4529 LNAI, 789–798. https://doi.org/10.1007/978-3-540-72950-1_77
Kaur, G., & Arora, S. (2018). Chaotic whale optimization algorithm. Journal of Computational Design and Engineering, 5(3), 275–284. https://doi.org/10.1016/j.jcde.2017.12.006
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kiani, F., Nematzadeh, S., Anka, F. A., & Findikli, M. A. (2023). Chaotic Sand Cat Swarm Optimization. Mathematics, 11(10). https://doi.org/10.3390/math11102340
Li, Q., Liu, S. Y., & Yang, X. S. (2020). Influence of initialization on the performance of metaheuristic optimizers. Applied Soft Computing Journal, 91. https://doi.org/10.1016/j.asoc.2020.106193
Lu, C., Gao, L., Li, X., Hu, C., Yan, X., & Gong, W. (2020). Chaotic-based grey wolf optimizer for numerical and engineering optimization problems. Memetic Computing, 12(4). https://doi.org/10.1007/s12293-020-00313-6
Luo, T., Xie, J., Zhang, B., Zhang, Y., Li, C., & Zhou, J. (2024). An improved levy chaotic particle swarm optimization algorithm for energy-efficient cluster routing scheme in industrial wireless sensor networks. Expert Systems with Applications, 241, 122780. https://doi.org/10.1016/j.eswa.2023.122780
Ma, Z. S. (2012). Chaotic populations in genetic algorithms. Applied Soft Computing, 12(8), 2409–2424.
Maaranen, H., Miettinen, K., & Mäkelä, M. M. (2004). Quasi-random initial population for genetic algorithms. Computers & Mathematics with Applications, 47(12). https://doi.org/10.1016/j.camwa.2003.07.011
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Mahmood, S., Bawany, N. Z., & Tanweer, M. R. (n.d.). A comprehensive survey of Whale Optimization Algorithm: Modifications and classification. Indonesian Journal of Electrical Engineering and Computer Science.
Mirjalili, S., & Lewis, A. (2016). Advances in Engineering Software The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Mohamed, A. A., Kamel, S., Hassan, M. H., & Zeinoddini-Meymand, H. (2024). CAVOA: A chaotic optimization algorithm for optimal power flow with facts devices and stochastic wind power generation. IET Generation, Transmission and Distribution, 18(1), 121–144. https://doi.org/10.1049/gtd2.13076
Molga, M., & Smutnicki, C. (2005). Test functions for optimization needs. Test Functions for Optimization Needs, 101, 48.
Ouertani, M. W., Manita, G., & Korbaa, O. (2021). Chaotic lightning search algorithm. Soft Computing, 25(3). https://doi.org/10.1007/s00500-020-05273-0
Ruiye, J., Tao, C., Songyan, W., & Ming, Y. (2018). Order whale optimization algorithm in rendezvous orbit design. Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018, 97–102. https://doi.org/10.1109/ICACI.2018.8377588
Saremi, S., Mirjalili, S., & Lewis, A. (2014). Biogeography-based optimisation with chaos. Neural Computing and Applications, 25(5). https://doi.org/10.1007/s00521-014-1597-x
Sayed, G. I., Darwish, A., & Hassanien, A. E. (2018). A New Chaotic Whale Optimization Algorithm for Features Selection. Journal of Classification, 35(2), 300–344. https://doi.org/10.1007/s00357-018-9261-2
Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20–34. https://doi.org/10.1016/j.engappai.2019.01.001
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