Designing Particle Swarm Optimization - Performance Comparison of Two Temporally Cumulative Fitness Functions in EPSO

H. Zhang and M. Ishikawa (Japan)


particle swarm optimization, realcoded genetic algorithm, elitism strategy, temporally cumulative fitness function, model selection


We present an Evolutionary Particle Swarm Optimization (EPSO) method for PSO model selection. It provides a new paradigm of meta-optimization that systematically es timates appropriate values of parameters in PSO for ef ficiently finding an optimal solution to a given optimiza tion problem. For investigating the characteristics, i.e., ex ploitation and exploration of the optimized PSO, this paper proposes to use two fitness functions in EPSO, which are a temporally cumulative fitness of the best particle and a temporally cumulative fitness of the entire swarm. Appli cations of the proposed method to a 2-dimensional opti mization problem well demonstrate its effectiveness. The obtained results indicate that the former fitness function can generate a PSO model with higher fitness, and the latter can generate a PSO model with faster convergence.

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