摘要
提出了一种基于改进多目标粒子群优化算法的水库防洪调度算法(MOPSO-RFC)。该算法采用下泄流量编码方式;设计了一种基于邻域最大拥挤距离的全局极值选择算子,以保持更好的种群多样性;设计了一种基于差分进化的精英种群自学习算子,以提高算法的求解效率。对陕西省安康水库两场典型洪水的调度研究结果表明,MOPSO-RFC算法获得了一组质量高、多样性好的防洪调度方案,有效实现了削减洪峰的目的。
In order to provide a more comprehensive information support to the decision making of the reservoir dispatch in the flood season, an improved multi-objective particle swarm optimization algorithm for reservoir flood dispatch（MOPSO-RFC）is proposed. In MOPSO-RFC the discharging downstream flow based coding is employed. In the proposed algorithm, a global best selection operator based on neighbor maximum crowding distance is deigned to maintain better diversity and elite population learning operator based on differential evolution is deigned to enhance its efficiency. Dispatching studies on the two typical floods of the Ankang reservoir in Shaanxi province indicate that MOPSO-RFC can obtain a set of scheduling schemes with good quality and variety. It can realize the pur- pose of reducing the flood peak.
出处
《计算机工程与应用》
CSCD
2012年第30期33-39,共7页
Computer Engineering and Applications
基金
国家自然科学基金(No.51109175)
中国博士后科学基金(No.20110490170)
陕西省科技攻关计划(No.2012K06-20)
陕西省教育厅科研计划项目资助(No.12JK0472)
关键词
水库防洪调度
多目标粒子群优化
差分进化
reservoir flood dispatch
Multi-Objective Particle Swarm Optimization
differential evolution
