Particle swarm optimization using adaptive local search

摘要

Particle swarm optimization (PSO) is a powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. 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.