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Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization
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DOI：10.15388/Informatica.2015.37.png)
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Abstract
En 中文
Classical evolutionary multi-objective optimization algorithms aim at finding an approximation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimization problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provided by the decision maker to find only desirable solutions satisfying his/her preferences on the Pareto front. Several scalarizing functions are used simultaneously so the several sets of solutions are obtained from the same preference information. In this paper, the experimental-comparative investigation of the proposed synchronous R-NSGA-II and original R-NSGA-II has been carried out. The results obtained are promising.
Keywords:
interactive multi-objective optimization
evolutionary multi-objective optimization
preference-based evolutionary algorithms
scalarizing function
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2.8
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402
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