基于分解的环境经济调度约束多目标进化算法

Jianping Yin, Chixin Xiao, Xun Zhou, Zhigang Xue, M. Yi, Wenjie Shu
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引用次数: 2

摘要

电力系统环境经济调度问题实际上是一个经典的约束多目标优化问题,属于进化优化范畴。然而,它的大部分属性并没有被它的土著帕特托Front研究过。同时,基于分解的多目标进化算法(MOEA/D)是多目标进化优化领域中一种新兴而又强大的方法,但由于在MOEA/D这种特殊框架中难以嵌入传统的处理约束的技巧,该算法如何在约束条件下运行还没有得到充分的验证。不同于非支配排序关系和简单的聚合,本文提出了一种新的基于分解思想的多目标进化方法和一些等式约束优化方法来处理EED问题。采用标准的IEEE 30总线六发电机测试系统,通过几个简单的参数设置来测试新算法的性能。实验结果表明,新方法优于或类似于许多最先进的多目标进化算法。高质量的实验结果验证了该方法的有效性和适用性。我们有充分的理由相信,新算法在现实世界的多目标优化问题上具有广阔的应用空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained multi-objective evolutionary algorithm based on decomposition for environmental/economic dispatch
The Environmental/Economic Dispatch EED puzzle of power system is actually a classic constrained multi-objective optimization problem in evolutionary optimization category. However, most of its properties have not been researched by its aboriginal Pateto Front. In a meanwhile, the multi-objective evolutionary algorithm based on decomposition(MOEA/D) is a well-known new rising yet powerful method in multi-objective evolutionary optimization domain, but how to run it under constrained conditions has not been testified sufficiently because it is not easy to embed traditional skills to process constraints in such special frame as MOEA/D. Different from non-dominated sorting relationship as well as simply aggregation, this paper proposes a new multi-objective evolutionary approach motivated by decomposition idea and some equality constrained optimization approaches to handle EED problem. The standard IEEE 30 bus six-generator test system is adopted to test the performance of the new algorithm with several simple parameter setting. Experimental results have shown the new method surpasses or performs similarly to many state-of-the-art multi-objective evolutionary algorithms. The high-quality experimental results have validated the efficiency and applicability of the proposed approach. It has good reason to believe that the new algorithm has a promising space over the real-world multi-objective optimization problems.
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