Acceptability of a Decision Maker to Handle Multi-objective Optimization on Design Space
Acceptability of a Decision Maker to Handle Multi-objective Optimization on Design Space
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DOI：
10.1109/scisisis50064.2020.9322679
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
M. Inoue;Hibiki Matsumoto;H. Takagi
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文献类型：
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作者：
M. Inoue;Hibiki Matsumoto;H. Takagi
我们引入了决策者在设计空间上处理进化多目标优化问题的可接受性，而大多数进化多目标优化研究试图在一个目标空间上找到多个解并将它们传递给决策者。与传统的EMO方法不同，我们的方法用可接受性的概念来决定Maker的模型，并将其引入EMO搜索中。特别是，当定性因素，如决策者对任务的经验和知识是评估的一部分时，这种方法效果很好。首先聚合每个目标的可接受性函数，聚合后的可接受性在目标空间上形成轮廓线，并映射到设计空间上。设计空间上的可接受等高线可以缩小解决方案的范围。我们可以在我们的实验中找到比在客观空间上搜索解决方案的传统方法更好的解决方案。
We introduce the acceptability of a decision maker to handle evolutionary multi-objective optimization (EMO) on design space, while most of EMO research tries to find many solutions on an objective space and passes them to a decision maker. Unlike this conventional EMO approaches, our approach decides maker's model with the concept of acceptability and introduces it in EMO search. Especially, this approach works well when qualitative factors, such as the decision maker's experience and knowledge on a task, are a part of evaluations. Acceptability functions for each of objectives are aggregated firstly, and the aggregated acceptability forms contours on an objective space and is mapped on a design space. The acceptability contours on a design space can narrow down the area of solutions. We could find better solutions in our experiments than the conventional approach of searching solutions on an objective space.