Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 10, Issue 3, Pages 281-295Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2005.857610
Keywords
composition benchmark functions; comprehensive learning particle swarm optimizer (CLPSO); global numerical optimization; particle swarm optimizer (PSO)
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This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes-available from http://www.ntu.edu.sgthome/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
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