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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.

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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, ZiliORCID iD for 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 Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30106223

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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.