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Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model

Titre	Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model
Publication Type	Conference Paper
Year of Publication	2017
Authors	Bouzbita, S, A. Afia, E, Faizi, R, Zbakh, M
Conference Name	Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016
Abstract	The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality. © 2016 IEEE.
URL	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013861130&doi=10.1109%2fCloudTech.2016.7847719&partnerID=40&md5=26cafb2d23ee70bf2f3552d7fec22e8a
DOI	10.1109/CloudTech.2016.7847719