基于稳定策略的粒子群优化算法

Particle Swarm Optimization Algorithm Based on Stable Strategy

为了解决传统粒子群算法易陷入局部最优解的问题,在借鉴生物学中"进化稳定策略"的基础上,对传统粒子群算法进行了改进,提出了基于稳定策略的粒子群算法。该算法的核心在于,通过稳定参数的设定,使种群中较优的一部分个体按照标准粒子群算法进行寻优,而对种群中其余部分的个体进行随机突变,以达到快速扩大搜索空间、稳定种群中个体多样性的目的。实验结果表明,该算法有效地避免了基本粒子群算法早熟现象的发生,提高了PSO对全局最优解的搜索能力和收敛速度。

An improved particle swarm optimization algorithm based on the evolutionarily stable strategy was proposed to avoid the problem of local optimum. The key to this algorithm lies in searching an optimum solution for optimal indi- viduals of the population according to the standard particle swarm algorithm by setting a stable parameter, while the rest part of population is mutated randomly. Therefore, the operator can keep the number of the best individuals at a stable level when it enlarges the search space. The performance of this algorithm shows that this algorithm can effec- tively avoid the premature convergence problem. Moreover, this algorithm improves the ability of searching an optimum solution and increases the convergent speed.