Hybrid particle swarm optimization for continuous problems

Abstract

In order to solve costly procedure of search and premature convergence for continuous function optimization problem, an improved particle swarm optimization algorithm combined with iterated local search (ILS) method is proposed. During the global search process, our algorithm can enhance the local search ability of particle swarm optimization thought adding random perturbation to local search. Some optimization tests of the standard benchmark function confirm that our method has a stronger ability of global optimization and a faster convergence.