A Particle Swarm Optimization Algorithm Based on Simulated Annealing

Abstract

The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.