An improved PSO for multimodal complex problem

Abstract

As the multimodal complex problem has many local optima, basic PSO is difficult to effectively solve this kind of problem. To conquer this defect, firstly, we adopt Monte Carlo method to simulate the fly trajectory of particle, and conclude the reason for falling into local optima. Then, by defining distance, average distance and maximal distance between particles, an adaptive control factor (Adaptive rejection factor, ARF) for pp and pg was proposed to increase the ability for escaping from local optima. In order to test the proposed strategy, three test benchmarks were selected to conduct the analysis of convergence property and statistical property. The simulation results show that particle swarm optimizer based on adaptive rejection factor (ARFPSO) can effectively avoid premature phenomenon. Therefore, ARFPSO is available for complex multimodal problems. © 2014 Springer International Publishing Switzerland.