Particle swarm algorithm with hybrid mutation strategy

A new particle swarm optimization (PSO) that incorporates a hybrid mutation strategy is proposed. In this paper we first use the Monte Carlo method to investigate the behavior of the particle in PSO. The results reveal the essence of the particle's trajectory during executions and the reasons why PSO has relative poor global searching ability especially in the last stage of evolution. Then we present a new hybrid particle swarm optimization which incorporates Henon map mutation operation (HPSO) so as to enhance the achievement of PSO. The new mutation strategy divides the mutation operator into global and local mutation operators, then it enables the particles to have stronger exploration ability and fast convergence rate. Sixteen benchmark functions are used to test the performance of HPSO. The results show that the new PSO algorithm performs better than the other hybrid PSO algorithms for each of the test functions. Meanwhile, HPSO is applied to a practical problem (i.e., the economic dispatch problem in a power system) with a satisfying result.