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Data Structures in Multi-Objective Evolutionary Algorithms

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Publié dans:Journal of Computer Science and Technology vol. 27, no. 6 (Nov 2012), p. 1197
Auteur principal: Altwaijry, Najwa
Autres auteurs: El Bachir Menai, Mohamed
Publié:
Springer Nature B.V.
Sujets:
Mathematical programming
Search process
Population
Computer science
Analysis
Populations
Studies
Data structures
Genetic algorithms
Optimization
Generations
Archives & records
Pareto optimization
Hierarchies
Multiple objective analysis
Pareto optimum
Evolutionary algorithms
Information science
Accès en ligne:Citation/Abstract
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Résumé:Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) are considered an attractive approach for solving MOPs, since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions (archives) may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.[PUBLICATION ABSTRACT]
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1296-y
Source:ABI/INFORM Global