Journal of Global Optimization
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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We present an Evolutionary Particle Swarm Optimization (EPSO) method for PSO model selection. It provides a new paradigm of meta-optimization that systematically estimates appropriate values of parameters in PSO for efficiently finding an optimal solution to a given optimization problem. For investigating the characteristics, i.e., exploitation 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. Applications of the proposed method to a 2-dimensional optimization 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.