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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article présente une approche pour améliorer la proximité et la diversité dans les algorithmes évolutifs multiobjectifs (MOEA). L’idée est de découvrir de nouvelles solutions non dominées dans le domaine prometteur de l’espace de recherche. Cela peut être réalisé en appliquant la mutation uniquement aux individus les plus convergents et les moins peuplés. En d’autres termes, la proximité et la diversité peuvent être améliorées car de nouvelles solutions non dominées se trouvent au voisinage des individus très convergents et moins encombrés. Les résultats empiriques sur les problèmes de sac à dos multiobjectifs (MKP) démontrent que l'approche proposée découvre un ensemble de solutions non dominées beaucoup plus proche du front de Pareto global tout en maintenant une meilleure répartition des solutions.
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Chang Wook AHN, Yehoon KIM, "Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2879-2882, October 2010, doi: 10.1587/transinf.E93.D.2879.
Abstract: This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2879/_p
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@ARTICLE{e93-d_10_2879,
author={Chang Wook AHN, Yehoon KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms},
year={2010},
volume={E93-D},
number={10},
pages={2879-2882},
abstract={This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.},
keywords={},
doi={10.1587/transinf.E93.D.2879},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Improving Proximity and Diversity in Multiobjective Evolutionary Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 2879
EP - 2882
AU - Chang Wook AHN
AU - Yehoon KIM
PY - 2010
DO - 10.1587/transinf.E93.D.2879
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - This paper presents an approach for improving proximity and diversity in multiobjective evolutionary algorithms (MOEAs). The idea is to discover new nondominated solutions in the promising area of search space. It can be achieved by applying mutation only to the most converged and the least crowded individuals. In other words, the proximity and diversity can be improved because new nondominated solutions are found in the vicinity of the individuals highly converged and less crowded. Empirical results on multiobjective knapsack problems (MKPs) demonstrate that the proposed approach discovers a set of nondominated solutions much closer to the global Pareto front while maintaining a better distribution of the solutions.
ER -