基于方向自学习遗传算法的水库优化调度  

Study on optimal operation of reservoir based on directional self-learning genetic algorithm

作　　者：马玉新[1,2] 解建仓[1] 罗军刚[1] 

机构地区：[1]西安理工大学水利水电学院,西安710048 [2]西安电子科技大学发展规划处,西安710071

出　　处：《水力发电学报》2009年第4期43-48,55,共7页Journal of Hydroelectric Engineering

基　　金：国家"863"计划资助项目(2006AA01A126);国家自然科学基金资助项目(05279041)

摘　　要：本文提出一种方向自学习遗传算法并用于水库优化调度问题。在传统遗传算法基础上引入方向自学习机制,克服标准遗传算法收敛速度慢,早熟收敛等缺点。该算法在局部搜索中引入方向信息,利用函数的伪梯度来指导搜索方向,提出一种消亡算子用以增加种群多样性,有效地避免了早熟收敛、提高了算法的收敛速度,避免了水库优化调度问题中的维数灾问题。实例计算表明,相对于传统的遗传算法,方向自学习遗传算法计算速度快、收敛性好,提高了计算效率,较好的解决了传统遗传算法求解水库优化调度时存在的一些问题。A directional self-learning genetic algorithm is proposed in this paper,and it is applied to the optimal operation of reservoir.A directional self-learning system is introduced to improve the traditional generic algorithm that is typically slow in convergence and subject to premature convergence.In the new algorithm,directional information or pseudo-gradient of the function is introduced to guide the local searching.In addition a withering operator is proposed to increase population diversity,thus premature convergence and dimensional disaster risk are eliminated,and greater convergence speed is achieved.A case study indicates that,in contrast with the traditional generic algorithm,the directional self-learning generic algorithm shows advantages in computing cost and convergence for the optimization of reservoir operation.

关 键 词：水利工程 水库 优化调度 遗传算法 方向自学习 

分 类 号：TV697.1[水利工程—水利水电工程]