Multi-objective ant colony optimization based on the Physarum-Inspired mathematical model for bi-objective traveling salesman problems Zhang, Zili, Gao, Chao, Lu, Yuxiao, Liu, Yuxin and Liang, Mingxin 2016, Multi-objective ant colony optimization based on the Physarum-Inspired mathematical model for bi-objective traveling salesman problems, PLoS One, vol. 11, no. 1, pp. 1-23, doi: 10.1371/journal.pone.0146709. Attached Files Name Description MIMEType Size Downloads zheng-multiobjective-2016.pdf Published version application/pdf 5.39MB 78 Title Multi-objective ant colony optimization based on the Physarum-Inspired mathematical model for bi-objective traveling salesman problems Formatted title  Multi-objective ant colony optimization based on the Physarum-Inspired mathematical model for bi-objective traveling salesman problems Author(s) Zhang, Zili orcid.org/0000-0002-8721-9333 Gao, Chao Lu, Yuxiao Liu, Yuxin Liang, Mingxin Journal name PLoS One Volume number 11 Issue number 1 Article ID e0146709 Start page 1 End page 23 Total pages 23 Publisher Public Library of Science Place of publication San Francisco, Calif. Publication date 2016-01-11 ISSN 1932-6203 Keyword(s) Algorithms Animals Ants Behavior, Animal Computer Simulation Models, Biological Models, Theoretical Pheromones Physarum Problem Solving Summary Bi-objective Traveling Salesman Problem (bTSP) is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs) have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM). PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs. Language eng DOI 10.1371/journal.pone.0146709 Field of Research MD Multidisciplinary HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2016, Zheng et al Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30106223 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Connect to published version (open access) 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 09 Feb 2018, 19:46:21 EST Fri, 09 Feb 2018, 19:47:04 EST Sat, 10 Feb 2018, 04:01:22 EST Thu, 06 Sep 2018, 06:01:47 EST Thu, 06 Sep 2018, 10:34:19 EST Thu, 06 Sep 2018, 10:39:09 EST Thu, 06 Sep 2018, 16:43:58 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 15 times in Scopus Search Google Scholar Access Statistics: 221 Abstract Views, 79 File Downloads  -  Detailed Statistics Created: Fri, 09 Feb 2018, 19:47:04 EST

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