The Performance Measurement of a Canonical Particle Swarm Optimizer with Diversive Curiosity
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.
Keywordscanonical particle swarm optimizer evolutionary particle swarm optimization real-coded genetic algorithm exploitation and exploration model selection specific and diversive curiosity
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