...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Water Resources Management >A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System
【24h】

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

机译：基于分解和优势的梯级水电系统长期发电调度的自适应多目标粒子群优化算法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade hydropower system (CHS). A novel adaptive multi-objective particle swarm optimization based on decomposition and dominance (D(2)AMOPSO) is developed in this paper to solve the MLGS problem. In D(2)AMOPSO, a constraint handling method based on repair strategy and individualconstraints and group constraints (ICGC) technique is embedded to address various constraints. An improved logistic map is adopted to initialize the population. During the evolutionary process, an improved Tchebycheff decomposition is introduced to select personal best and global best for each particle, and the non-dominated solutions found so far are stored in an external archive where crowding distance and elitist learning strategy are performed to improve its diversity. Meanwhile, an adaptive flight parameter adjustment mechanism based on Pareto entropy is adopted to balance the global exploration and local exploitation abilities of the population. A normal cloud mutation operator is used to keep the population diversity and escape local minima. In the case study of the Three Gorges Cascade hydropower system (TGC) under three typical years, the results of the proposed method and other four competitors show that D(2)AMOPSO can obtain better diversity and faster convergence solutions for the MLGS problem in less time.
机译：考虑生态流量需求的多目标长期发电调度（MLGS）对于梯级水电系统（CHS）中水资源的综合利用非常重要。为了解决MLGS问题，提出了一种基于分解和支配性的自适应多目标粒子群优化算法（D（2）AMOPSO）。在D（2）AMOPSO中，嵌入了一种基于修复策略，个体约束和组约束（ICGC）技术的约束处理方法来解决各种约束。采用改进的逻辑图来初始化种群。在进化过程中，引入了改进的Tchebycheff分解，以针对每个粒子选择个人最佳和全局最佳，并且到目前为止发现的非支配解都存储在外部档案库中，在其中执行拥挤距离和精英学习策略以改善其多样性。同时，采用基于帕累托熵的自适应飞行参数调整机制来平衡人口的全球勘探和局部开发能力。使用正常的云突变算子来保持种群多样性并逃避局部最小值。在三个典型年份下的三峡梯级水电系统（TGC）的案例研究中，所提出的方法和其他四个竞争对手的结果表明，D（2）AMOPSO可以在更少的时间内获得更好的分集和更快的MLGS问题收敛解。时间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号