Effect of chaos on the performance of spider wasp meta-heuristic optimization algorithm for high-dimensional optimization problems
Abstract
The spider wasp optimization (SWO) algorithm is a new nature-inspired meta-heuristic optimization algorithm based on the hunting, nesting, and mating behaviors of female spider wasps. This paper aims to apply chaos theory to the steps of the SWO algorithm in order to increase its convergence speed. Four versions of chaotic algorithms are constructed using the traditional spider wasp optimizer. The proposed chaotic spider wasp optimization (CSWO) algorithms select various chaotic maps and adjust the main parameters of the SWO optimizer to ensure the balance between exploration and exploitation stages. Furthermore, the constructed CSWO algorithms are benchmarked on eight well-known test functions divided into unimodal and multimodal problems. The experimental results and statistical analysis are carried out to demonstrate that CSWO algorithms are very suitable for searching optimal solutions for the benchmark functions. Specifically, the implementation of chaotic maps can significantly enhance the performance of the SWO algorithm. As a result, the new algorithm has high flexibility and outstanding robustness, which we can apply to engineering design problems.
Keywords
Meta-heuristic optimizer, chaotic map, spider wasp algorithm, benchmark function
Thanks
Thank you MMNSA
References
- [1] Deepa, R. and Venkataraman, R. Enhancing Whale Optimization Algorithm with Levy Flight for coverage optimization in wireless sensor networks. Computers & Electrical Engineering, 94, 107359, (2021).
- [2] Kitayama, S., Arakawa, M. and Yamazaki, K. Differential evolution as the global optimization technique and its application to structural optimization. Applied Soft Computing, 11(4), 3792- 3803, (2011).
- [3] Tomar, V., Bansal, M. and Singh, P. Metaheuristic algorithms for optimization: A brief review. Engineering Proceedings, 59(1), 238, (2024).
- [4] Alorf, A. A survey of recently developed metaheuristics and their comparative analysis. Engineering Applications of Artificial Intelligence, 117(A), 105622, (2023).
- [5] Rajwar, K., Deep, K. and Das, S. An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56, 13187-13257, (2023).
- [6] Sowmya, R., Premkumar, M. and Jangir, P. Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems. Engineering Applications of Artificial Intelligence, 128, 107532, (2024).
- [7] Crepinšek, M., Liu, S.H. and Mernik, M. Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 1-33, (2013).
- [8] Wolpert, D.H. and Macready, W.G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82, (1997).
- [9] Naik, P.A., Owolabi, K.M., Yavuz, M. and Zu, J. Chaotic dynamics of a fractional order HIV-1 model involving AIDS-related cancer cells. Chaos, Solitons & Fractals, 140, 110272, (2020).
- [10] Haneche, N. and Hamaizia, T. A three-dimensional discrete fractional-order HIV-1 model related to cancer cells, dynamical analysis and chaos control. Mathematical Modelling and Numerical Simulation with Applications, 4(3), 256-279, (2024).