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A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model

Liu,Y, Lu,Y, Gao,C, Zhang,Z and Tao,L 2014, A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model, in ICNC 2014 : Proceedings of the 10th International Conference on Natural Computation, IEEE, Piscataway, N.J., pp. 303-308, doi: 10.1109/ICNC.2014.6975852.

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Title A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model
Author(s) Liu,Y
Lu,Y
Gao,C
Zhang,ZORCID iD for Zhang,Z orcid.org/0000-0002-8721-9333
Tao,L
Conference name Natural Computation. Conference (10th : 2014 : Xiamen, China)
Conference location Xiamen, China
Conference dates 2014/8/19 - 2014/8/21
Title of proceedings ICNC 2014 : Proceedings of the 10th International Conference on Natural Computation
Editor(s) [Unknown]
Publication date 2014
Conference series International Conference on Natural Computation
Start page 303
End page 308
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Multi-objective ant colony optimization algorithms
Multi-objective traveling salesman problem
Physarum-inspired mathematical model
Summary Multi-objective traveling salesman problem (MOTSP) is an important field in operations research, which has wide applications in the real world. Multi-objective ant colony optimization (MOACO) as one of the most effective algorithms has gained popularity for solving a MOTSP. However, there exists the problem of premature convergence in most of MOACO algorithms. With this observation in mind, an improved multiobjective network ant colony optimization, denoted as PMMONACO, is proposed, which employs the unique feature of critical tubes reserved in the network evolution process of the Physarum-inspired mathematical model (PMM). By considering both pheromones deposited by ants and flowing in the Physarum network, PM-MONACO uses an optimized pheromone matrix updating strategy. Experimental results in benchmark networks show that PM-MONACO can achieve a better compromise solution than the original MOACO algorithm for solving MOTSPs.
ISBN 9781479951505
Language eng
DOI 10.1109/ICNC.2014.6975852
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072645

Document type: Conference Paper
Collection: School of Information Technology
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