The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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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
Les algorithmes évolutifs multi-objectifs sont largement utilisés dans de nombreux problèmes d’optimisation technique et applications d’intelligence artificielle. L'optimiseur de fourmis-lions est une méthode évolutive exceptionnelle, mais deux problèmes doivent être résolus pour l'étendre au domaine de l'optimisation multi-objectifs, l'un est de savoir comment mettre à jour l'archive Pareto et l'autre est de savoir comment choisir les lions d'élite et les fourmis dans les archives. Nous développons une nouvelle variante multi-objectif de l’optimiseur Ant Lion dans cet article. Une nouvelle mesure combinant la relation de dominance Pareto et l'information à distance des individus est proposée et utilisée pour résoudre le premier problème. Le concept de poids temporel est développé pour résoudre le deuxième problème. En outre, une opération de mutation est adoptée sur les solutions situées au milieu de l'archive pour améliorer encore ses performances. Onze fonctions, quatre autres algorithmes et quatre indicateurs sont utilisés pour évaluer la nouvelle méthode. Les résultats montrent que l’algorithme proposé présente de meilleures performances et une complexité temporelle moindre.
Yi LIU
Defense Innovation Institute
Wei QIN
Defense Innovation Institute
Jinhui ZHANG
Logistic Support Center of Chinese PLA General Hospital
Mengmeng LI
Defense Innovation Institute,Tianjin Artificial Intelligence Innovation Center
Qibin ZHENG
Academy of Military Science
Jichuan WANG
Defense Innovation Institute,Tianjin Artificial Intelligence Innovation Center
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Yi LIU, Wei QIN, Jinhui ZHANG, Mengmeng LI, Qibin ZHENG, Jichuan WANG, "Multi-Objective Ant Lion Optimizer Based on Time Weight" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 6, pp. 901-904, June 2021, doi: 10.1587/transinf.2021EDL8009.
Abstract: Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8009/_p
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@ARTICLE{e104-d_6_901,
author={Yi LIU, Wei QIN, Jinhui ZHANG, Mengmeng LI, Qibin ZHENG, Jichuan WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Objective Ant Lion Optimizer Based on Time Weight},
year={2021},
volume={E104-D},
number={6},
pages={901-904},
abstract={Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.},
keywords={},
doi={10.1587/transinf.2021EDL8009},
ISSN={1745-1361},
month={June},}
Copier
TY - JOUR
TI - Multi-Objective Ant Lion Optimizer Based on Time Weight
T2 - IEICE TRANSACTIONS on Information
SP - 901
EP - 904
AU - Yi LIU
AU - Wei QIN
AU - Jinhui ZHANG
AU - Mengmeng LI
AU - Qibin ZHENG
AU - Jichuan WANG
PY - 2021
DO - 10.1587/transinf.2021EDL8009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2021
AB - Multi-objective evolutionary algorithms are widely used in many engineering optimization problems and artificial intelligence applications. Ant lion optimizer is an outstanding evolutionary method, but two issues need to be solved to extend it to the multi-objective optimization field, one is how to update the Pareto archive, and the other is how to choose elite and ant lions from archive. We develop a novel multi-objective variant of ant lion optimizer in this paper. A new measure combining Pareto dominance relation and distance information of individuals is put forward and used to tackle the first issue. The concept of time weight is developed to handle the second problem. Besides, mutation operation is adopted on solutions in middle part of archive to further improve its performance. Eleven functions, other four algorithms and four indicators are taken to evaluate the new method. The results show that proposed algorithm has better performance and lower time complexity.
ER -