Adaptive particle swarm optimization with rotational invariance

Robustness and adaptability are necessary for metaheuristics because they are applied in various environment such as black-box optimization. In this paper, adding a parameter adjustment rule to Particle Swarm Optimization with rotational invariance using correlativity (CRI-PSO). we develop an adaptive CRI-PSO to improve the adaptability and maintain the robustness. First, using the swarm activity as an index evaluating the search state, we analyze a parameter of CRI-PSO based on intensification and diversification. Second, the parameter adjustment rule is based on evaluation and control of the search state. The rule controls the search state to realize intensification and diversification by adap-tively adjusting the parameter. Also, the rule is designed so as to the adaptive CRI-PSO satisfies several transformation invariances of the solution space PSO not having. The performances (robustness and adaptability) of the adaptive CRI-PSO are verified through numerical experiments for typical benchmark functions comparing the adaptive CRI-PSO with three types of conventional PSOs. © 2019 The Institute of Electrical Engineers of Japan.