基于并行混合差分进化算法的梯级水库群优化调度研究

发布时间：2018-03-17 07:06

本文选题：梯级水库群　切入点：优化调度　出处：《水力发电学报》2017年06期 　论文类型：期刊论文

【摘要】：以差分进化算法(DE)为基本框架,结合混沌算法(CA)和蛙跳算法(SLFA)各自局部搜索优势以及多核并行计算技术(PC),提出一种新的并行混合差分进化算法(PHDE),即将DE与CA、SLFA进行有机融合,分别对精英个体进行混沌局部搜索和对较差个体进行蛙跳局部更新,且差分进化运算、混沌局部搜索和蛙跳局部更新均采用PC,以有效缩短计算时间。PHDE具有三点优势:一是保留了DE简单易行、收敛迅速的特点;二是继承了CA、SLFA的遍历性,能够避免早熟收敛现象;三是通过合理的并行模式,有效降低了计算时间。典型测试函数表明了PHDE的可行性、高效性和鲁棒性。实例研究表明,PHDE具有较好的优化性能和计算效率,为高效求解水库群优化调度问题提供了一种可行途径。
[Abstract]:Taking the differential evolution algorithm (DEE) as the basic framework, combining the local search advantages of chaos algorithm (CAA) and leapfrog algorithm (SLFA) and the multi-core parallel computing technology, a new parallel hybrid differential evolution algorithm (PHDEG) is proposed, which combines DE with CASLFA. Chaotic local search for elite individuals and leapfrogging local updating for poor individuals are carried out respectively, and differential evolution operations are performed. The chaotic local search and leapfrogging local update both adopt PCS, which can effectively shorten the computing time. PHDE has three advantages: firstly, the advantages of DE are simple and quick convergence, the other is inheriting the ergodicity of CASLFA, which can avoid the phenomenon of premature convergence. The typical test function shows the feasibility, high efficiency and robustness of PHDE. The case study shows that PHDE has better optimization performance and computational efficiency. It provides a feasible way to solve the optimal operation problem of reservoir group efficiently.
【作者单位】：长江勘测规划设计研究有限责任公司水利规划研究院;华中科技大学水电与数字化工程学院;
【基金】：国家重点研发计划重点专项项目(2016YFC0402202)
【分类号】：TV697.12

【相似文献】