难度可控和可扩展约束多目标测试问题

2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration Pub Date : 2015-12-03 DOI:10.1109/ICIICII.2015.105

Zhun Fan, Wenji Li, Xinye Cai, Hui Li, Kaiwen Hu, Haibin Yin

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引用次数: 4

摘要

在本文中，我们提出了一个通用工具包来构造具有三种不同约束函数的约束多目标优化问题(cops)。在此基础上，提出了8个约束多目标优化问题，命名为CMOP1-CMOP8。由于整个搜索空间中可行区域的比例决定了约束多目标优化问题的难度，我们提出了4个可行区域比例非常低的测试例cmp3 -6。为了研究所提出的测试实例的困难，我们对两种流行的cmoea - MOEA/D-CDP和NSGA-II-CDP进行了实验，并分析了它们的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Difficulty Controllable and Scalable Constrained Multi-objective Test Problems

In this paper, we propose a general toolkit to construct constrained multi-objective optimisation problems (CMOPs) with three different kinds of constraint functions. Based on this toolkit, we suggested eight constrained multi-objective optimisation problems named CMOP1-CMOP8. As the ratio of feasible regions in the whole search space determines the difficulty of a constrained multi-objective optimisation problem, we propose four test instances CMOP3-6, which have very low ratio of feasible regions. To study the difficulties of proposed test instances, we make some experiments with two popular CMOEAs - MOEA/D-CDP and NSGA-II-CDP, and analysed their performances.

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来源期刊

2015 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration

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