一种改进的全局粒子群优化算法

为了改善粒子群优化算法的优化性能, 提出一种改进的全局粒子群优化(IGPSO) 算法. 该算法基于开采能力和搜索能力相均衡的思想提出全局邻域搜索策略和扰动策略, 使算法减少陷入局部极值的可能性, 同时以一定概率对全局最优粒子进行摄动操作, 加快算法收敛. 与其他智能算法相比较, 测试结果从寻优精度、收敛速度和非参数统计显著性方面验证了IGPSO 算法的有效性.

An improved global particle swarm optimization

In order to improve the performance of particle swarm optimization(PSO) algorithm, an improved global particle swarm optimization(IGPSO) is presented. Based on a balance between exploitation and exploration ability, the global neighborhood search strategy and disturbance strategy are proposed to reduce the possibility of falling into local minima. Meanwhile, a perturbation operation with probabilities is implemented in the global best particle, which aims at accelerating the convergence speed. The test results demonstrate the effectiveness of the IGPSO algorithm in terms of accuracy, convergence speed, and nonparametric statistical significance when compared with other state-of-the-art intelligent algorithms.