An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
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DOI：
10.1109/tevc.2013.2281535
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发表时间：
2014-08-01
影响因子：
14.3
通讯作者：
Jain, Himanshu
中科院分区：
文献类型：
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作者：
Deb, Kalyanmoy;Jain, Himanshu
已经开发了多目标优化算法，使用进化优化方法，并证明了他们的利基对各种实际问题，主要涉及两个和三个目标，现在有一个不断增长的需求，发展进化多目标优化（EMO）算法处理多目标（有四个或更多的目标）优化问题。在本文中，我们认识到一些最近的努力，并讨论了一些可行的方向发展一个潜在的EMO算法解决多目标优化问题。此后，我们提出了一个基于参考点的多目标进化算法NSGA-II框架（我们称之为NSGA-III），强调人口成员是非支配的，但接近一组提供的参考点。建议NSGA-III适用于许多多目标测试问题，3至15个目标，并与最近建议的EMO算法（MOEA/D）的两个版本进行比较。虽然这两个MOEA/D方法的工作原理以及不同类别的问题，建议NSGA-III被认为是本文中考虑的所有问题产生令人满意的结果。本文介绍了无约束问题的结果，并考虑在处理多目标优化问题的约束和其他专业的续集。
Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.