Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems-Reference-Cited by-同舟云学术

Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications - LOPAL '18
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Author:

Bouzbita Safae¹,El Afia Abdellatif¹,Faizi Rdouan¹

Affiliation:

1. ENSIAS - Mohammed V University, Rabat, Morocco

Publisher

ACM Press

Reference25 articles.

1. Dorigo, M., Birattari, M., and Stutzle, T. 2006. Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28--39.

2. Dorigo, M and Di Caro, G. 1999. Ant colony optimization: a new meta-heuristic. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on (Vol. 2, pp. 1470--1477). IEEE.

3. Bouzbita, S., El Afia, A., and Faizi, R. Hidden Markov Model classifier for the adaptive ACS-TSP pheromone parameter. In bioinspired Heuristics for Optimization (pp. 143--156) Springer, Cham.

4. Bouzbita, S., El Afia, A., Faizi, R., and Zbakh, M. 2016. Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model. In Cloud Computing Technologies and Applications (CloudTech), 2016 2nd International Conference on (pp. 344--349). IEEE.

5. Bouzbita, S., El Afia, A., and Faizi, R. 2016. A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on (pp. 633--638). IEEE.

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