Designing Particle Swarm Optimization: performance comparison of two temporally cumulative fitness functions in EPSO
Hong Zhang, Masumi Ishikawa · International conference on Artificial intelligence and applications · 2008
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.