A Hybrid Particle Swarm Optimization with Adaptive Local Search

Particle swarm optimization (PSO) has shown its good search ability in many optimization problems. However, PSO often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO, namely LSPSO, to solve this problem by employing an adaptive local search operator. Experimental results on 8 well-known benchmark problems show that LSPSO achieves better results than the standard PSO, PSO with Gaussian mutation and PSO with Cauchy mutation on majority of test problems.