Constraint-handling method for multi-objective function optimization: Pareto descent repair operator

Authors:
Ken Harada;Jun Sakuma;Isao Ono;Shigenobu Kobayashi
Affiliations:
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama-shi, Kanagawa-ken, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama-shi, Kanagawa-ken, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama-shi, Kanagawa-ken, Japan;Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama-shi, Kanagawa-ken, Japan
Venue:
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Year:
2007

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Abstract

Among the multi-objective optimization methods proposed so far, Genetic Algorithms (GA) have been shown to be more effective in recent decades. Most of such methods were developed to solve primarily unconstrained problems. However, many real-world problems are constrained, which necessitates appropriate handling of constraints. Despite much effort devoted to the studies of constraint-handling methods, it has been reported that each of them has certain limitations. Hence, further studies for designing more effective constraint-handling methods are needed. For this reason, we investigated the guidelines for a method to effectively handle constraints. Based on these guidelines, we designed a new constraint-handling method, Pareto Descent Repair operator (PDR), in which ideas derived from multi-objective local search and gradient projection method are incorporated. An experiment comparing GA that use PDR and some of the existing constraint-handling methods confirmed the effectiveness of PDR.