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A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search. (English) Zbl 1390.90582
Summary: Incorporation of a decision maker’s preferences into multi-objective evolutionary algorithms has become a relevant trend during the last decade, and several preference-based evolutionary algorithms have been proposed in the literature. Our research is focused on improvement of a well-known preference-based evolutionary algorithm R-NSGA-II by incorporating a local search strategy based on a single agent stochastic approach. The proposed memetic algorithm has been experimentally evaluated by solving a set of well-known multi-objective optimization benchmark problems. It has been experimentally shown that incorporation of the local search strategy has a positive impact to the quality of the algorithm in the sense of the precision and distribution evenness of approximation.

MSC:
90C59 Approximation methods and heuristics in mathematical programming
90C29 Multi-objective and goal programming
Software:
MOEA/D; SPEA2
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