FRPSO: Fletcher–Reeves based particle swarm optimization for multimodal function optimization

Abstract

Particle swarm optimization (PSO) is a population-based optimization tool that is inspired by the collective intelligent behavior of birds seeking food. It can be easily implemented and applied to solve various function optimization problems. However, relatively few researchers have explored the potential of PSO for multimodal problems. Although PSO is a simple, easily implemented, and powerful technique, it has a tendency to get trapped in a local optimum. This premature convergence makes it difficult to find global optimum solutions for multimodal problems. A hybrid Fletcher---Reeves based PSO (FRPSO) method is proposed in this paper. It is based on the idea of increasing exploitation of the local optimum, while maintaining a good exploration capability for finding better solutions. In FRPSO, standard PSO is used to update the particle's current position, which is then further refined by the Fletcher---Reeves conjugate gradient method. This enhances the performance of standard PSO. The results of experiments conducted on seventeen benchmark test functions demonstrate that the proposed method shows superior performance on a set of multimodal functions when compared with standard PSO, a genetic algorithm (GA) and fitness distance ratio PSO (FDRPSO).