Genetic Algorithm for Multi-objective Optimization Using GDEA
Genetic Algorithm for Multi-objective Optimization Using GDEA
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DOI：
10.1007/11539902_49
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Yeboon Yun;Min Yoon;H. Nakayama
中科院分区：
文献类型：
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作者：
Yeboon Yun;Min Yoon;H. Nakayama
近年来，许多遗传算法作为一种近似生成多目标优化问题帕累托边界（帕累托最优解集）的方法得到了发展。在多目标遗传算法中，有两个重要的问题：如何为每个个体分配适应度，以及如何使个体多样化。为了克服这些问题，本文提出了一种基于广义数据包络分析（GDEA）的多目标遗传算法。数值算例表明，该方法能够以较少的函数求值次数生成分布良好且近似良好的Pareto边界。
Recently, many genetic algorithms (GAs) have been developed as an approximate method to generate Pareto frontier (the set of Pareto optimal solutions) to multi-objective optimization problem. In multi-objective GAs, there are two important problems : how to assign a fitness for each individual, and how to make the diversified individuals. In order to overcome those problems, this paper suggests a new multi-objective GA using generalized data envelopment analysis (GDEA). Through numerical examples, the paper shows that the proposed method using GDEA can generate well-distributed as well as well-approximated Pareto frontiers with less number of function evaluations.