Effects of Simulated Annealing Strategy on Swarm Intelligence Algorithm

Swarm intelligence algorithm (SI) is a kind of stochastic search algorithm based on swarm. Similar to other evolutionary algorithm, when solving the complicated multimodal problem using SI, it is easy to have premature convergence. So, to promote the optimization of swarm intelligence algorithm, the typical algorithm (Particle swarm optimizer) of swarm intelligence algorithm is selected to explore some strategies how to improve the performance. In this paper, we explore the follow research: firstly, the mutation operation is introduced to produce new learn example for each individual in itself evolution process; secondly, in the view of the idea of simulated annealing, the range strategy of fitness of each individual is proposed; finally, to make best use of each individual information, the comprehensive learning strategy is adopted to improve each individual evolution mechanism.