Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 23, Issue 6, Pages 972-986Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2019.2896967
Keywords
Pareto optimization; Geometry; Convergence; Evolutionary computation; Systematics; Indexes; Constrained multiobjective optimization; constraint-handling techniques (CHTs); evolutionary algorithms (EAs); performance comparisons; test suite
Funding
- Innovation-Driven Plan in Central South University [2018CX010]
- National Natural Science Foundation of China [61673397]
- Hunan Provincial Natural Science Fund for Distinguished Young Scholars [2016JJ1018]
- Beijing Advanced Innovation Center for Intelligent Robots and Systems [2018IRS06]
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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.
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