Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization
Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization
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DOI：
10.1109/smc.2018.00135
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发表时间：
2018-10
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影响因子：
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通讯作者：
Naoki Masuyama;Yuki Tanigaki;Y. Nojima;H. Ishibuchi
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文献类型：
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作者：
Naoki Masuyama;Yuki Tanigaki;Y. Nojima;H. Ishibuchi
近年来，进化多目标优化算法（EMO）经常被用于同时优化多个相互冲突的目标函数的工程问题。EMO算法可以为用户提供多个Pareto最优解。在EMO算法的实际应用中，考虑了两种情况。一种是决策者从EMO过程后得到的解中选择一个解。另一种是决策者利用这些解来分析设计变量与相应问题的目标函数之间的关系。在本文中，我们应用模糊遗传学为基础的机器学习的第二种情况下，以产生if-then基于规则的分类器，代表设计变量和目标函数之间的关系。我们还利用这种方法在EMO过程中，预先筛选候选后代的解决方案。分类器检测非有前途的后代解决方案。然后，它们在其适应度评估之前被丢弃，使得计算资源仅用于有希望的解决方案。我们将此方法应用于一个工程问题，并研究其效果的EMO算法的搜索性能。
In recent years, evolutionary multiobjective optimization (EMO) algorithms have frequently been used for engineering problems with some conflicting objective functions to be simultaneously optimized. EMO algorithms can provide a number of Pareto optimal solutions to users. Two scenarios are considered in the practical use of EMO algorithms. One is that a decision maker selects a single solution from the obtained ones after the EMO process. The other is that a decision maker utilizes the solutions to analyze the relationship between design variables and objective functions of the corresponding problem. In this paper, we apply fuzzy genetics-based machine learning to the second scenario in order to generate if-then rule-based classifiers which represent the relationship between design variables and objective functions. We also utilize this method during the EMO process to pre-screen candidate offspring solutions. The classifier detects non-promising offspring solutions. Then, they are discarded before their fitness evaluation, so that the computation resource is used only for promising solutions. We apply this method to one engineering problem and examine its effect on the search performance of an EMO algorithm.