Purpose
– In this paper, a new method to improve the performance of particle swarm optimization is proposed.
Design/methodology/approach
– This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added.
Findings
– The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality.
Originality/value
– This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.
Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem