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Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons
IEEE Transactions on Evolutionary Computation ( IF 14.3 ) Pub Date : 2019-12-01 , DOI: 10.1109/tevc.2019.2896967
Zhongwei Ma , Yong Wang

For solving constrained multiobjective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community for the past two decades. Generally, the effectiveness of an algorithm for CMOPs is evaluated by artificial test problems. However, after a brief review of current artificial test problems, we have found that they are not well-designed and fail to reflect the characteristics of real-world applications (e.g., small feasibility ratio). Thus, in this paper, we first propose a new constraint construction method to facilitate the systematic design of test problems. Then, on the basis of this method, we design a new test suite consisting of 14 instances, which covers diverse characteristics extracted from real-world CMOPs and can be divided into four types. Considering that the comprehensive performance comparisons among the constraint-handling techniques (CHTs) remain scarce, we choose several representative CHTs and compare their performance on our test suite. The performance comparisons identify the strengths and weaknesses of different CHTs on different types of CMOPs and provide guidelines on how to select/design a CHT in a specific scenario.

中文翻译:

进化约束多目标优化:测试套件构建和性能比较

为了解决约束多目标优化问题(CMOP),在过去的二十年里,进化计算研究界提出了许多算法。通常,CMOP 算法的有效性是通过人工测试问题来评估的。然而,在简要回顾当前的人工测试问题后,我们发现它们的设计并不完善,无法反映现实世界应用的特点(例如,可行性比小)。因此,在本文中,我们首先提出了一种新的约束构造方法,以方便测试问题的系统设计。然后,在该方法的基础上,我们设计了一个由 14 个实例组成的新测试套件,它涵盖了从现实世界 CMOP 中提取的各种特征,可分为四种类型。考虑到约束处理技术(CHT)之间的综合性能比较仍然很少,我们选择了几个有代表性的 CHT 并在我们的测试套件上比较它们的性能。性能比较确定了不同 CHT 在不同类型 CMOP 上的优势和劣势,并提供了有关如何在特定场景中选择/设计 CHT 的指南。
更新日期:2019-12-01
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