A benchmark test suite for evolutionary many-objective optimization
A benchmark test suite for evolutionary many-objective optimization
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DOI：
10.1007/s40747-017-0039-7
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发表时间：
2017-03-01
影响因子：
5.8
通讯作者：
Yao, Xin
中科院分区：
文献类型：
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作者：
Cheng, Ran;Li, Miqing;Yao, Xin
在现实世界中，面对三个以上目标的优化问题并不少见。这类问题称为多目标优化问题(MAOP)，对进化计算领域提出了巨大的挑战。传统的基于Pareto的多目标进化算法在处理多目标优化问题上的失败激发了各种新的方法。然而，与算法设计的快速发展相比，算法的性能研究和比较却鲜有人关注。一些为多目标优化设计的测试问题集仍然在多目标优化中占主导地位。本文精心选择(或修改)了15个具有不同性质的测试问题，构建了一个基准测试集，旨在通过提出一组能够很好地代表各种现实场景的测试问题来促进进化多目标优化(EMAO)的研究。此外，还提供了一个开放源代码的软件平台，提供了用户友好的图形用户界面，以便于实验执行和数据观察。
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation.