Abstract
Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm widely used to solve various numerical optimization problems. However, the PSO can easily fall into local optimum when it solves complex optimization problems. An improved chaotic particle swarm optimization algorithm (ICPSO) is proposed to address this problem. A chaotic perturbation strategy is used to enhance the algorithm's ability to explore the global. Besides, an escape strategy is utilized to increase the diversity of the particle population. The performance of the ICPSO algorithm is verified by testing five benchmark functions, and the results show that the algorithm has good global exploration and convergence ability.
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Acknowledgment
This work is supported by the Guangdong Youth Characteristic Innovation Project (2021KQNCX120), the Natural Science Foundation of Guangdong Province of China (2020A1515010784), the Natural Science Project of Guangdong University of Science and Technology (GKY-2021KYYBK-20), the General Project of Science and Technology of Dongguan Social Development (20231800910352), the Natural Science Project of Guangdong University of Science and Technology (XJ2022003501), the University Distinguishing Innovation Project of Guangdong Provincial Department of Education (2021KTSCX149), and Key Project of Science and Technology of Dongguan Social Development (20211800905512).
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Wang, Y., Liu, S., Su, W. (2023). An Improved Chaos-Based Particle Swarm Optimization Algorithm. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_13
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DOI: https://doi.org/10.1007/978-981-99-1549-1_13
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