An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls

•Interactive evolutionary algorithm for handling many objective optimization problems.•Incorporates decision maker preferences in intermediate steps of the algorithm.•Polyhedral cones and sets are used to modify domination and perform a focussed search.•Successfully handles optimization problems up...

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Veröffentlicht in:European journal of operational research Jg. 233; H. 3; S. 674 - 688
Hauptverfasser: Sinha, Ankur, Korhonen, Pekka, Wallenius, Jyrki, Deb, Kalyanmoy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 16.03.2014
Elsevier Sequoia S.A
Schlagworte:
Algorithms
Computational efficiency
Decision analysis
Decision making
Evolutionary
Evolutionary algorithms
Evolutionary multi-objective optimization
Interactive
Interactive multi-objective optimization
Mathematical analysis
Mathematical functions
Mathematical problems
Multiple criteria decision-making
Optimization
Optimization algorithms
Pareto optimum
Studies
Multiple criteria decision-making
Evolutionary multi-objective optimization
Interactive multi-objective optimization
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•Interactive evolutionary algorithm for handling many objective optimization problems.•Incorporates decision maker preferences in intermediate steps of the algorithm.•Polyhedral cones and sets are used to modify domination and perform a focussed search.•Successfully handles optimization problems up to five objectives. This paper presents a preference-based method to handle optimization problems with multiple objectives. With an increase in the number of objectives the computational cost in solving a multi-objective optimization problem rises exponentially, and it becomes increasingly difficult for evolutionary multi-objective techniques to produce the entire Pareto-optimal front. In this paper, an evolutionary multi-objective procedure is combined with preference information from the decision maker during the intermediate stages of the algorithm leading to the most preferred point. The proposed approach is different from the existing approaches, as it tries to find the most preferred point with a limited budget of decision maker calls. In this paper, we incorporate the idea into a progressively interactive technique based on polyhedral cones. The idea is also tested on another progressively interactive approach based on value functions. Results are provided on two to five-objective unconstrained as well as constrained test problems.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2013.08.046