Differential Evolution versus Genetic Algorithms in Multiobjective Optimization
This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb}...
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Published in | Evolutionary Multi-Criterion Optimization pp. 257 - 271 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variants DEMO\documentclass[12pt]{minimal}
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\begin{document}$^\text{NS-II}$\end{document}, DEMO\documentclass[12pt]{minimal}
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\begin{document}$^\text{SP2}$\end{document} and DEMO\documentclass[12pt]{minimal}
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\begin{document}$^\text{IB}$\end{document}. Experimental results on 16 numerical multiobjective test problems show that on the majority of problems, the algorithms based on differential evolution perform significantly better than the corresponding genetic algorithms with regard to applied quality indicators. This suggests that in numerical multiobjective optimization, differential evolution explores the decision space more efficiently than genetic algorithms. |
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ISBN: | 9783540709275 3540709274 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-540-70928-2_22 |