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A multi-objective ant colony optimization algorithm based on the physarum-inspired mathematical model
conference contribution
posted on 2014-01-01, 00:00 authored by Y Liu, Y Lu, C Gao, Zili ZhangZili Zhang, L TaoMulti-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.
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Event
Natural Computation. Conference (10th : 2014 : Xiamen, China)Pagination
303 - 308Publisher
IEEELocation
Xiamen, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-08-19End date
2014-08-21ISBN-13
9781479951505Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICNC 2014 : Proceedings of the 10th International Conference on Natural ComputationUsage metrics
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