Particle Swarm Optimization Algorithm Based on Stable Strategy
基于稳定策略的粒子群优化算法

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 individuals 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 effectively avoid the premature convergence problem Moreover, this algorithm improves the ability of searching an optimum solution and increases the convergent speed.