Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning

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Abstract

This paper proposes an Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning (AH-CLPSO) based on the previous proposed Learning Particle Swarm Optimizer (CLPSO) [1], which is good at multimodal problems but converges slow on single modal problems. A self-adaptation technique is introduced to adjust the learning probability adaptively in the search process and the historical information is used in the velocity update equation to search more effectively. The experiment results show that the history learning strategy and the adaptation technique improves the performance of CLPSO on problems which need fast convergence and achieve comparable results on the problems requiring slow convergence.