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A self-adaptive control strategy of population size for ant colony optimization algorithms

Liu, Yuxin, Liu, Jindan, Li, Xianghua and Zhang, Zili 2016, A self-adaptive control strategy of population size for ant colony optimization algorithms, Lecture notes in computer science, vol. 9712, no. Part 1, pp. 443-450, doi: 10.1007/978-3-319-41000-5_44.

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Title A self-adaptive control strategy of population size for ant colony optimization algorithms
Author(s) Liu, Yuxin
Liu, Jindan
Li, Xianghua
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name Lecture notes in computer science
Volume number 9712
Issue number Part 1
Start page 443
End page 450
Total pages 8
Publisher Springer
Place of publication Switzerland
Publication date 2016
ISSN 0302-9743
1611-3349
Keyword(s) ant colony optimization
population size
self-adaptive control
travelling salesman problem
Summary Ant colony optimization (ACO) algorithms often have a lower search efficiency for solving travelling salesman problems (TSPs). According to this shortcoming, this paper proposes a universal self-adaptive control strategy of population size for ACO algorithms. By decreasing the number of ants dynamically based on the optimal solutions obtained from each interaction, the computational efficiency of ACO algorithms can be improved dramatically. Moreover, the proposed strategy can be easily combined with various ACO algorithms because it's independent of operation details. Two well-known ACO algorithms, i.e., ant colony system (ACS) and max-min ant system (MMAS), are used to estimate the performance of our proposed strategy. Some experiments in both synthetic and benchmark datasets show that the proposed strategy reduces the computational cost under the condition of finding the same approximate solutions.
Notes Advances in swarm intelligence - Proceedings of 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016
Language eng
DOI 10.1007/978-3-319-41000-5_44
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090712

Document type: Journal Article
Collection: School of Information Technology
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