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A self-adaptive control strategy of population size for ant colony optimization algorithms
journal contribution
posted on 2016-01-01, 00:00 authored by Y Liu, J Liu, X Li, Zili ZhangZili ZhangAnt 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.
History
Journal
Lecture notes in computer scienceVolume
9712Issue
Part 1Pagination
443 - 450Publisher
SpringerLocation
SwitzerlandPublisher DOI
ISSN
0302-9743eISSN
1611-3349Language
engNotes
Advances in swarm intelligence - Proceedings of 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016Publication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2016, SpringerUsage metrics
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