Abstract
Many engineering optimization problems involve handling constraints. Existing constraint-handling methods, dealing with all constraints simultaneously as a whole, may become less effective when the number of constraints is large, termed many-constraint optimization problems (MCOPs). Since different constraints usually pose different degrees of difficulty to optimization problems (the constraint satisfying order may also be defined by a decision-maker), intuitively, a potential way is to progressively introduce each constraint into the search wherein the constraint-handling order becomes crucial. However, MCOPs and the problem-solver are far from being well investigated. This study therefore fills in this research gap. First, MCOPs are formulated, followed by an analysis of the difficulty of MCOPs. Then the concept of constraint-ranking is introduced. Based on the ranking results, a novel framework, i.e., cascaded constraint-handling (CCH), that follows “the most interesting first” principle is proposed to solve MCOPs. This implicitly enables the search to start from both interior and exterior of the feasible region. To demonstrate the effectiveness of the CCH framework, first an MCOP benchmark suite is designed. Then the penalty function based constraint-handling technique with and without the CCH is compared. Experimental results clearly show the superiority of the CCH framework.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Nos. 61773390 and 71571187) and the Outstanding Natural Science Foundation of Hunan Province (2017JJ1001).
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Ming, M., Wang, R., Zhang, T. (2019). Evolutionary Many-Constraint Optimization: An Exploratory Analysis. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_14
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DOI: https://doi.org/10.1007/978-3-030-12598-1_14
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