The performance verification of an evolutionary canonical particle swarm optimizer.

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.