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

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...

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Bibliographic Details
Published in:2014 10th International Conference on Natural Computation (ICNC) pp. 303 - 308
Main Authors: Yuxin Liu, Yuxiao Lu, Chao Gao, Zili Zhang, Li Tao
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2014
Subjects:
Ant colony optimization
Cities and towns
Conductivity
Electron tubes
Mathematical model
Measurement
Multi-Objective Ant Colony Optimization Algorithms
Multi-Objective Traveling Salesman Problem
Optimization
Physarum-Inspired Mathematical Model
ISSN:2157-9555
Online Access:Get full text
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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 multi-objective network ant colony optimization, denoted as PM-MONACO, 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.
ISSN:2157-9555
DOI:10.1109/ICNC.2014.6975852