文章摘要
面向梯级水库多目标优化调度的进化算法研究
Research on evolutionary algorithms for multi-objective optimal operation of cascade reservoirs
投稿时间:2020-04-02  修订日期:2020-11-20
DOI:
中文关键词: 多目标优化  梯级水库调度  大规模优化  高维多目标优化
英文关键词: multi-objective optimization  cascade reservoir operation  large-scale optimization  many-objective optimization
基金项目:国家重点研发计划项目(2016YFC0402309);“十三五”国家重点研发计划课题(2016YFC0402208)
作者单位E-mail
纪昌明 华北电力大学 水利与水电工程学院 cmji@ncepu.edu.cn 
马皓宇 华北电力大学 水利与水电工程学院 940467366@qq.com 
彭杨 华北电力大学 水利与水电工程学院  
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中文摘要:
      实际工程中以梯级水库多目标优化调度为代表的大规模高维多目标优化问题,其优化难度是一般方法所难以应对的。为此本文提出一种新型的多目标粒子群算法LMPSO,其包含了基于超体积指标Ik h的适应值分配方法与基于问题变换的搜索空间降维策略,以有效处理问题的高维目标向量与大规模决策变量。将该算法应用于溪洛渡-向家坝梯级水库的中长期多目标优化调度中,并与4种知名算法的计算结果进行对比分析,验证LMPSO在求解该类问题上的卓越性能。由此为多目标优化调度高质量非劣解集的获取提供一种可靠的方法,并为下一步的多目标调度决策提供有力的数据支持。
英文摘要:
      In practical projects, the large-scale many-objective optimization problems, represented by multi-objective optimal operation of cascade reservoirs, are difficult to be solved by general methods. Therefore, this paper proposes a new multi-objective particle swarm optimization (LMPSO), which includes both a fitness assignment approach based on the hypervolume indicator Ik h and a strategy to reduce search space dimensions based on problem transformation, in order to effectively deal with high-dimensional objective vectors and large-scale decision variables. The algorithm is applied to the medium-and-long-term multi-objective optimal operation of Xiluodu-Xiangjiaba cascade reservoirs, and the calculation results are comparatively analyzed with four established algorithms, to verify the excellent performance of LMPSO. It provides not only a reliable method to obtain high-quality Pareto solution sets in multi-objective optimal operation but also strong data support for the subsequent multi-objective operation decision-making.
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