Using opposition-based learning to improve the performance of particle swarm optimization

Particle swarm optimization (PSO) is a stochastic, population-based optimization method, which has been applied successfully to a wide range of problems. However, PSO is computationally expensive and suffers from premature convergence. In this paper, opposition-based learning is used to improve the performance of PSO. The performance of the proposed approaches is investigated and compared with PSO when applied to eight benchmark functions. The experiments conducted show that opposition-based learning improves the performance of PSO.