Abstract:

Particle swarm optimization (PSO) is an optimization algorithm based on swarm intelligence.Based on introducing PSO’s theory and flow, this paper analyzes the phenomenon that it suffers from premature convergence, longer search time and lower precision when dealing with complex problems. An improved particle swarm optimization algorithm based on new mutation operators(NMPSO) is presented.The mutation operator is compared with the current particles, and the better one will be selected. So the diversity of population is improved, which can help the algorithm avoid premature convergence efficiently. The comparative simulation results on five benchmark functions verify that NMPSO improves PSO’s global search capability, convergence rate and precision.

Key words: evolutionary computation;particle swarm optimization(PSO);mutation operator;global optimum