Self-evolving ant colony optimization and its application to traveling salesman problem

Material type: 文書・図像類

Author: Zhou, Xiao-Fanほか

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Publication date: 2012-12

Material Format: Digital

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Note (General)：: Ant colony optimization (ACO) algorithms are a recently developed, popula- tion-based approach which has been successfully applied to combinatorial op...

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Material Type: 文書・図像類
Title: Self-evolving ant colony optimization and its application to traveling salesman problem
Author/Editor: Zhou, Xiao-Fan
Wang, Rong-Long
Author Heading: Zhou, Xiao-Fan
Wang, Rong-Long
Publication Date: 2012-12
Publication Date (W3CDTF): 2012-12
Periodical title: International Journal of Innovative Computing,Information and Control
No. or year of volume/issue: 8 12
Volume: 8
Issue: 12
Pages: 8311-8321
ISSN (Periodical Title): ISSN : 1349-4198
Text Language Code: eng
Subject Heading: Ant colony optimization
Combinatorial optimization problems
Traveling salesman problem
Self-evolving strategy
Target Audience: 一般
Note (General): Ant colony optimization (ACO) algorithms are a recently developed, popula- tion-based approach which has been successfully applied to combinatorial optimization problems. However, in the ACO algorithms, it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this paper we proposed an improved ACO algorithm in which a self-evolving strategy is adopted so as to acquire a trade-off between intensification and diversification. With this strategy we make each ant evolve by means of multiple pheromone deposition, the total concentration of which includes two parts, that is, one part is deposited by several best ants of the last iteration and the ant that has searched the global-best solution by the current iteration, and the other part is deposited temporarily by each ant itself of the last iteration as well. The proposed algorithm is tested by simulating Traveling Salesman Problem (TSP) and experimental results show that Self-evolving Ant Colony Optimization has superior performance when compared with other existing ACO algorithms.
Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=23066&item_no=1&attribute_id=22&file_no=1
https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=23066&item_no=1&attribute_id=22&file_no=1
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ