摘要： 针对粒子群优化算法易早熟收敛的缺点，提出一种自适应粒子群优化算法(ASPO)，将物种的概念引入种群多样性测度中，利用种群多样性信息对惯性权重进行非线性的调整，并引入速度变异算子和位置交换算子，增强算法的全局收敛性能。将APSO算法应用于电力系统无功优化，对IEEE-30节点系统进行仿真计算，仿真结果表明，系统网损从5.988 MW降到4.889 MW，下降率为18.36%，算法的收敛精度和收敛稳定性均较当前常用方法有明显的提高。

关键词: 粒子群优化, 种群多样性, 自适应变异, 无功优化

Abstract: This paper presents Adaptive Particle Swarm Optimization(APSO) algorithm to solve the precocious convergence problem of Particle Swarm Optimization(PSO) algorithm. The notion of species is introduced into population diversity measure. Inertia weight is nonlinearly adjusted by using population diversity information. Velocity mutation factor and position crossover factor are both introduced and the global performance is improved. The algorithm is applied in reactive power optimization. Simulation results of the standard IEEE-30-bus power system indicate that active power losses are reduced form 5.988 MW to 4.889 MW(18.36% reduction) and APSO is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.

Key words: Particle Swarm Optimization(PSO), population diversity, adaptive mutation, reactive power optimization

中图分类号: 

• TP301.6