基于动态种群结构的粒子群算法及仿真研究

Particle Swarm Optimization Algorithm Based on Dynamic Population Structure and Simulation

针对标准粒子群算法易陷入局部最优而早熟的问题,提出了一种基于动态种群结构的粒子群算法。该算法在种群结构中引入小世界网络模型,由于网络模型的演化,使算法具有动态的种群结构,从而保持了种群的多样性。同时为了使粒子尽可能地分布在不同的搜索空间,在网络模型演化过程中考虑了结点的个体价值。为了加快算法的收敛速度,在进化后期采用全局模型粒子群算法。通过对三个经典测试函数优化问题的数值仿真并与其它方法进行比较,结果表明了算法的有效性和实用性。

To overcome premature searching by standard particle swarm optimization （PSO） algorithm, a new dynamic PSO was proposed. In the algorithm, the small world network model is introduced into population structure, and the topology structure is dynamical with the evolvement of small world network. So the population diversity is enhanced. The individual value is applied to evolution of small world network for particles can distribute in different search space. In order to perform a better local search, a global PSO version was used in the end of the search. The effectiveness and practicability is demonstrated by the simulation results of three functions optimization comparing with other methods.