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CLC number: TP202
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Received: 2001-12-29
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An adaptive ant colony system algorithm for continuous-space optimization problems
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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.
@article{title="An adaptive ant colony system algorithm for continuous-space optimization problems",
author="Li Yan-jun, Wu Tie-jun",
journal="Journal of Zhejiang University Science A",
volume="4",
number="1",
pages="40-46",
year="2003",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2003.0040"
}
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%A Li Yan-jun
%A Wu Tie-jun
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2003.0040
TY - JOUR
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
Y1 - 2003
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2003.0040
Abstract: 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.
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