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. Attached Files Name Description MIMEType Size Downloads Title A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model Author(s) Liu,Y Lu,Y Gao,C 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 Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 23 Apr 2015, 13:07:48 EST Thu, 23 Apr 2015, 13:31:01 EST Fri, 24 Apr 2015, 16:37:54 EST Fri, 08 May 2015, 12:46:12 EST Fri, 08 May 2015, 13:51:52 EST Fri, 08 May 2015, 14:13:42 EST Fri, 08 May 2015, 15:30:45 EST Fri, 08 May 2015, 16:00:48 EST Mon, 11 May 2015, 13:21:16 EST Wed, 07 Feb 2018, 14:01:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 215 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 08 May 2015, 12:46:12 EST

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