Abstract
In order to increase the diversity of bare-bone particle swarm optimization (BBPSO) population search range, enhance the ability to jump out of local optimum, we propose an atomic retrospective learning bare-bone particle swarm optimization (ARBBPSO) algorithm based on BBPSO. Different from the renewal strategy of BBPSO, inspired by electron motion around protons in ARBBPSO, we use a strategy of motion around nuclei to increase population diversity. At the same time, the retrospective learning strategy is used to allow the proton particles to have a chance to correct errors during the process of updating, thus allowing the population to have a chance to evade falling into a local optimum. To verify the performance of the proposed algorithm, 29 benchmark functions of CEC2017 are chosen to compare with four well-known BBPSO-based algorithms. The experimental results indicate that ARBBPSO is superior to several other algorithms for improving BBPSO from a comprehensive consideration.
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Zhou, G., Guo, J., Yan, K., Zhou, G., Li, B. (2023). An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_14
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