Double space based multiobjective evolutionary algorithm

Junchi Liang; Jia You; Guoqiang Han; Le Li

doi:10.1109/ICMLC.2012.6359571

Double space based multiobjective evolutionary algorithm

Junchi Liang, Jia You, Guoqiang Han, Le Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Recently, solving multiobjective problems are gaining more and more attention due to its useful applications in the area of engineering, bioinformatics, pattern recognition. Although there exist a lot of multiobjective evolutionary algorithms (MOEAs) for solving multiobjective problems, few of them considers the evolutionary process in both the solution space and the objective space. In the paper, we will propose a new hybrid multiobjective evolutionary algorithm named as double space based multiobjective evolutionary algorithms (DS-MOEA) to perform multiobjective optimization. Compared with traditional MOEAs, DS-MOEA not only considers the evolutionary process in the solution space, but also takes into account the knowledge learning process in the objective space. The results in the experiment illustrate that DS-MOEA works well during the process of solving multiobjective problems.

Original language	English
Title of host publication	Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages	1406-1411
Number of pages	6
Volume	4
DOIs	https://doi.org/10.1109/ICMLC.2012.6359571
Publication status	Published - 31 Dec 2012
Event	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China Duration: 15 Jul 2012 → 17 Jul 2012

Conference

Conference	2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/Territory	China
City	Xian, Shaanxi
Period	15/07/12 → 17/07/12

Keywords

Evolutionary algorithm
Multiobjective optimization
Objective space

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Human-Computer Interaction

More information

10.1109/ICMLC.2012.6359571

Cite this

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abstract = "Recently, solving multiobjective problems are gaining more and more attention due to its useful applications in the area of engineering, bioinformatics, pattern recognition. Although there exist a lot of multiobjective evolutionary algorithms (MOEAs) for solving multiobjective problems, few of them considers the evolutionary process in both the solution space and the objective space. In the paper, we will propose a new hybrid multiobjective evolutionary algorithm named as double space based multiobjective evolutionary algorithms (DS-MOEA) to perform multiobjective optimization. Compared with traditional MOEAs, DS-MOEA not only considers the evolutionary process in the solution space, but also takes into account the knowledge learning process in the objective space. The results in the experiment illustrate that DS-MOEA works well during the process of solving multiobjective problems.",

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Liang, J, You, J, Han, G & Li, L 2012, Double space based multiobjective evolutionary algorithm. in Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. vol. 4, 6359571, pp. 1406-1411, 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012, Xian, Shaanxi, China, 15/07/12. https://doi.org/10.1109/ICMLC.2012.6359571

Double space based multiobjective evolutionary algorithm. / Liang, Junchi; You, Jia; Han, Guoqiang et al.
Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012. Vol. 4 2012. p. 1406-1411 6359571.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - You, Jia

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AB - Recently, solving multiobjective problems are gaining more and more attention due to its useful applications in the area of engineering, bioinformatics, pattern recognition. Although there exist a lot of multiobjective evolutionary algorithms (MOEAs) for solving multiobjective problems, few of them considers the evolutionary process in both the solution space and the objective space. In the paper, we will propose a new hybrid multiobjective evolutionary algorithm named as double space based multiobjective evolutionary algorithms (DS-MOEA) to perform multiobjective optimization. Compared with traditional MOEAs, DS-MOEA not only considers the evolutionary process in the solution space, but also takes into account the knowledge learning process in the objective space. The results in the experiment illustrate that DS-MOEA works well during the process of solving multiobjective problems.

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Double space based multiobjective evolutionary algorithm

Abstract

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Keywords

ASJC Scopus subject areas

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