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中文摘要：
基于帕累托最优概念的多目标进化算法在电力系统无功优化领域已有广泛应用，但目前通过某种单～算法求解的方式由于进化算子的唯一性，难以保证进化过程不同寻优阶段的普适性和鲁棒性，因此提出一种基于多种进化算法自适应选择的多目标无功优化方法。通过分析已有多目标进化算法的特征，考虑协调性与互补性，建立包含4类算法的备选池；在进化过程不同阶段根据寻优性能白适应地确定备选算法的使用比例，从而综合多种算法的性能优势，提高整体寻优效率。以IEEE30节点标准系统的多目标无功优化为算例，从帕累托前沿、外部解及c指标等方面与已有单一算法的优化结果进行比较，表明所提新方法在整个进化过程中都显示出了更优的收敛特性。
英文摘要：
Multi-objective evolutionary algorithms （MOEAs） based on Pareto optimal are commonly introduced to optimal reactive power flow （ORPF）. However, existing algorithms for multi-objective ORPF （MORPF）, which adopt evolution operator with single search characteristics, can not obtain universal and robust performance in different phases of optimization process. In this paper, a new method based on multiple evolutionary algorithms with adaptive selection strategies （MEAASS） for MORPF was proposed. Based on analysis of characteristics of state-of-the-art MOEAs, and considering the rules of consistency and complementation, candidate algorithm pool containing four different algorithms was presented. By means of adaptively favoring individual algorithms that exhibit higher reproductive success during the search, MEAASS simultaneously merged the strengths of multiple algorithms for population evolution. Based on the test case of IEEE 30 bus system, from the view point of Pareto fronts, outer solutions and C metric, the computing performance of MEAASS was compared with existing popular algorithms. The numerical simulations demonstrate that MEAASS can obtain better performance of convergence during the entire optimization process.
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