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Journal of Zhejiang University SCIENCE A 2003 Vol.4 No.1 P.40-46


An adaptive ant colony system algorithm for continuous-space optimization problems

Author(s):  Li Yan-jun, Wu Tie-jun

Affiliation(s):  Institute of Intelligent Systems and Decision Making, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yjlee@iipc.zju.edu.cn

Key Words:  Ant colony algorithm, Continuous-space optimization, Pheromone update strategy

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Li Yan-jun, Wu Tie-jun. An adaptive ant colony system algorithm for continuous-space optimization problems[J]. Journal of Zhejiang University Science A, 2003, 4(1): 40-46.

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journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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T1 - An adaptive ant colony system algorithm for continuous-space optimization problems
A1 - Li Yan-jun
A1 - Wu Tie-jun
J0 - Journal of Zhejiang University Science A
VL - 4
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SP - 40
EP - 46
%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2003.0040

ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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