A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm

When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front....

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Vydáno v:Journal of global optimization Ročník 62; číslo 1; s. 101 - 129
Hlavní autoři: Ruiz, Ana Belén, Saborido, Rubén, Luque, Mariano
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2015
Springer
Springer Nature B.V
Témata:
Algorithms
Analysis
Classification
Computer Science
Decision making
Evolutionary algorithms
Genetic algorithms
Mathematical analysis
Mathematical programming
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Optimization algorithms
Pareto optimality
Pareto optimum
Population
Preferences
Real Functions
Studies
Vectors (mathematics)
Weighting
United States > US
Pareto optimal solutions
Achievement scalarizing function
Multiobjective optimization
Evolutionary algorithm
Reference point approach
ISSN:0925-5001, 1573-2916
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Popis
Shrnutí:When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the region of interest of the Pareto optimal front. In this paper, we suggest a preference-based EMO algorithm called weighting achievement scalarizing function genetic algorithm (WASF-GA) , which considers the preferences of the decision maker (DM) expressed by means of a reference point. The main purpose of WASF-GA is to approximate the region of interest of the Pareto optimal front determined by the reference point, which contains the Pareto optimal objective vectors that obey the preferences expressed by the DM in the best possible way. The proposed approach is based on the use of an achievement scalarizing function (ASF) and on the classification of the individuals into several fronts. At each generation of WASF-GA, this classification is done according to the values that each solution takes on the ASF for the reference point and using different weight vectors. These vectors of weights are selected so that the vectors formed by their inverse components constitute a well-distributed representation of the weight vectors space. The efficiency and usefulness of WASF-GA is shown in several test problems in comparison to other preference-based EMO algorithms. Regarding a metric based on the hypervolume, we can say that WASF-GA has outperformed the other algorithms considered in most of the problems.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-014-0214-y