For improving the search performance of a canonical particle swarm optimizer (CPSO), we propose a newly canonical particle swarm optimizer with diversive curiosity (CPSO/DC). A crucial idea here is to introduce diversive curiosity into the CPSO to comprehensively manage the trade-off between exploitation and exploration for alleviating stagnation. To demonstrate the effectiveness of the proposed method, computer experiments on a suite of five-dimensional benchmark problems are carried out. We investigate the characteristics of the CPSO/DC, and compare the search performance with other methods. The obtained results indicate that the search performance of the CPSO/DC is superior to that by EPSO, ECPSO and RGA/E, but is inferior to that by PSO/DC for the Griewank and Rastrigin problems. © 2010 Springer-Verlag.
CITATION STYLE
Zhang, H., & Zhang, J. (2010). The performance measurement of a canonical particle swarm optimizer with diversive curiosity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6145 LNCS, pp. 19–26). https://doi.org/10.1007/978-3-642-13495-1_3
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