The fitness evaluation strategy in particle swarm optimization

The fitness evaluation strategy in particle swarm optimization

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Article ID: iaor20115522
Volume: 217
Issue: 21
Start Page Number: 8655
End Page Number: 8670
Publication Date: Jul 2011
Journal: Applied Mathematics and Computation
Authors: , , ,
Keywords: Particle swarm optimisation
Abstract:

The particle swarm optimization (PSO) computational method has recently become popular. However, it has limitations. It may trap into local optima and cause the premature convergence phenomenon, especially for multimodal and high‐dimensional problems. In this paper, we focus on investigating the fitness evaluation in terms of a particle’s position. Particularly, we find that the fitness evaluation strategy in the standard PSO has two drawbacks, i.e., ‘two steps forward and one step back’ and ‘two steps back and one step forward’. In addition, we propose a general fitness evaluation strategy (GFES), by which a particle is evaluated in multiple subspaces and different contexts in order to take diverse paces towards the destination position. As demonstrations of GFES, a series of PSOs with GFES are presented. Experiments are conducted on several benchmark optimization problems. The results show that GFES is effective at handling multimodal and high‐dimensional problems.

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