2010 Special Issue: The performance verification of an evolutionary canonical particle swarm optimizer

Authors:
Hong Zhang;Masumi Ishikawa
Affiliations:
Department of Brain Science and Engineering, Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology, Japan;Department of Brain Science and Engineering, Graduate School of Life Science & Systems Engineering, Kyushu Institute of Technology, Japan
Venue:
Neural Networks
Year:
2010

Citing 10
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Abstract

We previously proposed to introduce evolutionary computation into particle swarm optimization (PSO), named evolutionary PSO (EPSO). It is well known that a constricted version of PSO, i.e., a canonical particle swarm optimizer (CPSO), has good convergence property compared with PSO. For further improving the search performance of an CPSO, we propose in this paper a new method called an evolutionary canonical particle swarm optimizer (ECPSO) using the meta-optimization proposed in EPSO. The ECPSO is expected to be an optimized CPSO in that optimized values of parameters are used in the CPSO. We also introduce a temporally cumulative fitness function into the ECPSO to reduce stochastic fluctuation in evaluating the fitness function. Our experimental results indicate that (1) the optimized values of parameters are quite different from those in the conventional CPSO; (2) the search performance by the ECPSO, i.e., the optimized CPSO, is superior to that by CPSO, OPSO, EPSO, and RGA/E except for the Rastrigin problem.