An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
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DOI：
10.1109/tevc.2013.2281534
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发表时间：
2014-08-01
影响因子：
14.3
通讯作者：
Deb, Kalyanmoy
中科院分区：
文献类型：
--
作者：
Jain, Himanshu;Deb, Kalyanmoy
在前体论文中，建议了一种基于NSGA-II框架的多目标优化方法（NSGA-III），并应用于单独使用盒子约束的许多无约束的测试和实际问题。在本文中，我们扩展了NSGA-III，以解决一般约束的多目标优化问题。在此过程中，我们还建议三种类型的受约束测试问题，这些问题可扩展到任何数量的目标，并为多目标优化器提供不同类型的挑战。先前建议的MOEA/D算法也扩展到解决受约束的问题。使用受约束的NSGA-III和受约束的MOEA/D的结果显示出前者的边缘，尤其是在解决大量目标的问题方面。此外，NSGA-III算法在更新时具有自适应性，并随时使用新的参考点。与原始的NSGA-III相比，与原始的NSGA-III相比，所得的自适应NSGA-III可提供帕累托最佳前端的密集表示。这是原始的NSGA-III纸，共同提出并充分测试了一种可行的进化多目标优化算法，用于处理约束和不受约束的问题。这些研究应鼓励研究人员在进化多目标优化中使用和进一步关注。
In the precursor paper, a many-objective optimization method (NSGA-III), based on the NSGA-II framework, was suggested and applied to a number of unconstrained test and practical problems with box constraints alone. In this paper, we extend NSGA-III to solve generic constrained many-objective optimization problems. In the process, we also suggest three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many-objective optimizer. A previously suggested MOEA/D algorithm is also extended to solve constrained problems. Results using constrained NSGA-III and constrained MOEA/D show an edge of the former, particularly in solving problems with a large number of objectives. Furthermore, the NSGA-III algorithm is made adaptive in updating and including new reference points on the fly. The resulting adaptive NSGA-III is shown to provide a denser representation of the Pareto-optimal front, compared to the original NSGA-III with an identical computational effort. This, and the original NSGA-III paper, together suggest and amply test a viable evolutionary many-objective optimization algorithm for handling constrained and unconstrained problems. These studies should encourage researchers to use and pay further attention in evolutionary many-objective optimization.