Evolutionary many-objective optimization

Evolutionary many-objective optimization
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DOI：
10.1109/gefs.2008.4484566
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发表时间：
2008-03
期刊：
2008 3rd International Workshop on Genetic and Evolving Systems
影响因子：
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通讯作者：
H. Ishibuchi;Noritaka Tsukamoto;Y. Nojima
H. Ishibuchi;Noritaka Tsukamoto;Y. Nojima
中科院分区：
其他
文献类型：
--
作者：
H. Ishibuchi;Noritaka Tsukamoto;Y. Nojima

文献摘要

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在本文中，我们首先解释了基于Pareto优势的进化多目标优化算法(如NSGA-II和SPEA)难以解决多目标问题的原因。然后，我们解释了最近提出的用进化算法处理多目标问题的建议。通过对具有两个、四个和六个目标的多目标背包问题的计算实验，验证了一些建议。最后，我们讨论了多目标遗传模糊系统(即使用多目标遗传算法设计基于模糊规则的系统)的可行性。
In this paper, we first explain why many-objective problems are difficult for Pareto dominance-based evolutionary multiobjective optimization algorithms such as NSGA-II and SPEA. Then we explain recent proposals for the handling of many-objective problems by evolutionary algorithms. Some proposals are examined through computational experiments on multiobjective knapsack problems with two, four and six objectives. Finally we discuss the viability of many-objective genetic fuzzy systems (i.e., the use of many-objective genetic algorithms for the design of fuzzy rule-based systems).