摘要： 针对粒子群优化算法在进化中随种群多样性降低易出现早熟收敛等问题，结合全局-局部最优模型，提出一种改进的全局-局部参数最优粒子群优化算法。利用全局-局部最优惯性权重及全局-局部最优加速度常数，简化速度更新方程，使算法性能得到改善。将该算法应用于电力系统无功优化中，仿真结果表明，网损平均值更低，寻优性能更好，优化的网损值集中在较小的区间。

关键词: 粒子群优化算法, 惯性权重, 加速常数

Abstract: Aiming at the disadvantages such as prematurity in Particle Swarm Optimization(PSO) algorithm because of the decrease of swarm diversity, an improved PSO algorithm named GLBest-PSO(Global-Local Best PSO) is proposed combining the global best and local best model. The algorithm incorporates global-local best inertia weight and global-local best acceleration coefficient to simplify the velocity equation and the performance of the algorithm is improved. It is used in the reactive power optimization in power system. Simulation results show that its average transmission loss is lower, with better optimization performance and smaller area of optimized transmission loss values.

Key words: Particle Swarm Optimization(PSO) algorithm, inertia weight, acceleration coefficient

中图分类号: 

• TP312