水电站水库优化调度混沌粒子群算法  

Chaotic particle swarm optimization algorithm for optimal operation of hydropower station

作　　者：国海涛[1] 刘性泉 岳峻[3] 赵含雨 

机构地区：[1]山东商业职业技术学院信息技术学院,济南250103 [2]山东黄河河务局东平湖管理局,山东泰安271000 [3]鲁东大学信息科学与工程学院,山东烟台264025

出　　处：《南水北调与水利科技》2014年第6期181-183,共3页South-to-North Water Transfers and Water Science & Technology

基　　金：国家科技支撑计划项目(2011BAD20B01);山东省科技发展计划项目(2011GGB01138)

摘　　要：针对粒子群算法易陷入局部最优问题,从数学角度分析了粒子群算法易陷入局部最优的理论原因,提出一种自适应混沌变异粒子群算法,对陷入局部最优的粒子产生变异,增加算法的遍历性、种群的多样性,以跳出局部最优解,用来解决水库优化调度问题。与现有算法相比,自适应混沌变异粒子群算法计算快,稳定性强,既避免了粒子群算法陷入局部最优,同时在一定程度上又保证了收敛性。The particle swarm optimization（PSO） algorithm can easily fall into the local optimization.In this paper,the theoretic reasons of local optimization were analyzed from the mathematical perspective,and a new self-adaptive chaotic mutation PSO algorithm was proposed and applied to a hydropower station.The results showed that the new algorithm can mutate the particles which fall into the local optimization,increase the algorithm ergodicity and swarm diversity,and find the global optimization solution,which is useful for the optimal operation of reservoir.Compared with the current algorithms,the self-adaptive chaotic mutation PSO algorithm has fast computation and strong stability,avoids the local optimization,and ensures the convergence.

关 键 词：水库优化调度 粒子群优化 混沌 变异 自适应 局部最优 收敛性 

分 类 号：TV697[水利工程—水利水电工程]