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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| Vydáno v: | 2014 10th International Conference on Natural Computation (ICNC) s. 303 - 308 |
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| Hlavní autoři: | , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.08.2014
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| Témata: | |
| ISSN: | 2157-9555 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISSN: | 2157-9555 |
| DOI: | 10.1109/ICNC.2014.6975852 |