一种基于多簇结构的高斯动态粒子群优化算法

A Multi-Cluster Structure Based Gaussian Dynamic Particle Swarm Optimization Algorithm

分析高斯动态粒子群优化算法(GDPSO)中新的种群产生方式的特点,针对传统粒子群优化算法中全局最优模型收敛速度快但易陷入局部最优、局部最优模型收敛速度较慢的缺点,提出一种新的粒子群信息共享方式——多簇结构.该算法在簇内部实现粒子间信息的高度共享,而在簇之间则通过松散的连接实现信息的传递,以协调 GDPSO 算法的勘探和开采能力.通过典型的 Benchmark 函数优化问题测试并分析经典拓扑以及多簇结构在GDPSO 算法中的性能,仿真实验结果表明,采用特定多簇结构的 GDPSO 算法收敛速度和稳定性显著提高,同时全局搜索能力明显增强.

The method of population generation in Gaussian dynamic particle swarm optimization algorithm （GDPSO） is analyzed detailedly. Aiming at the problem of premature convergence of Gbest version and the slow search speed of Lbest version in original particle swarm optimization, a novel neighborhood topology structure called multi-cluster structure is proposed. In the proposed population structure, particles in one cluster share the information with each other, and clusters exchange their experiences through loose connection between particles. Thus, neighborhood topology is designed to coordinate exploration and exploitation. GDPSO, with several population topologies including the multi-cluster structure, is tested on four benchmark functions which are commonly used in the evolutionary computation. Experimental results show that the GDPSO with the proposed neighborhood topology can significantly speed up the convergence and efficiently improve the global search ability.