基于后验满足度的模糊多目标优化三步优化方法

2011 International Conference on Control, Automation and Systems Engineering (CASE) Pub Date : 2011-07-30 DOI:10.1109/ICCASE.2011.5997701

Chaofang Hu, Na Wang

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引用次数: 1

摘要

针对模糊多目标优化问题，提出了一种基于后验满意度的三步满足方法。首先，采用多目标遗传算法求出均匀分布Pareto最优集;然后，提出消去优化方法，将该集合约简为M-Pareto最优集。最后，利用具有有效性准则的模糊均值聚类对得到的集合进行分类，构造具有代表性的M-Pareto最优子集，使DM能够方便地选择优选结果。算例表明了该方法的有效性。

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Three-Step Optimization Method Based on Posteriori Satisfying Degree for Fuzzy Multiple Objective Optimization

A three-step satisfying method based on posteriori satisfying degree is proposed for fuzzy multiple objective optimization problem. Firstly, the uniformly distributed Pareto optimal set is acquired by the multiple objective genetic algorithm. Then, the eliminating optimization method is presented to reduce this set to the M-Pareto optimal set. Finally, the fuzzy mean clustering with the validity criteria is used to classify the obtained set to construct the representative M-Pareto optimal subset such that DM can choose the preferred result easily. The numerical example shows the power of the proposed method.

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2011 International Conference on Control, Automation and Systems Engineering (CASE)

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