Abstract:

A new improved particle swarm optimization (IPSO) algorithm, which guarantees to converge to the global optimization solution with probability one is proposed. During the running time, the mutation probability for the current particle is determined by the variance of the individual’s concentration and convergence function. The ability of IPSO to break away from the local optimum is greatly improved by the mutation. The concept of adaptive acceleration factor is introduced to the IPSO. In this manner, the global and local search capability can be coordinated to make for locating the global optimum quickly. Finally, IPSO is applied to optimizing several test functions and shock resistance of equipment research. Test results show that IPSO has great advantage of the convergence property, and the shock resistance of equipment research shows that IPSO is effective.

Key words: particle swarm optimization algorithm；mutation；adaptive；shock resistance of equipment