@article {Bouzbita2021253,
	title = {A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System},
	journal = {Studies in Computational Intelligence},
	volume = {906},
	year = {2021},
	note = {cited By 1},
	pages = {253-267},
	abstract = {We propose in this paper a Hidden Markov Model~(HMM) approach to avoid premature convergence of ants in the Ant Colony System~(ACS) algorithm. Indeed, the proposed approach was modelled as a classifier method to control the convergence through the dynamic adaptation of the α parameter that weighs the relative influence of the pheromone. The implementation was tested on several Travelling Salesman Problem~(TSP) instances with different number of cities. The proposed approach was compared with the standard ACS and the existing fuzzy logic~in the literature. The experimental results illustrate that the proposed method shows better performance. {\textcopyright} 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-58930-1_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097964733\&doi=10.1007\%2f978-3-030-58930-1_17\&partnerID=40\&md5=220c02fd7287bc58dc79db86db73282b},
	author = {Bouzbita, S. and Afia, A.E. and Faizi, R.}
}
@conference {Bouzbita2017633,
	title = {A novel based Hidden Markov Model approach for controlling the ACS-TSP evaporation parameter},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2017},
	note = {cited By 0},
	pages = {633-638},
	abstract = {The aim of this paper is to propose a new method capable of dynamically controlling the evaporation parameter in an Ant Colony System (ACS) using a Hidden Markov Model. The purpose is to improve the performance of ACS by controlling the exploration and exploitation in the search space. To this end, two HMM approaches are proposed. The first is a training method that best suits the observed data of the Hidden Markov Model. The second is a method that dynamically controls the adapted parameter by applying several processes. To test our algorithm we used a set of Travelling Salesman Problem (TSP) instances. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ICMCS.2016.7905544},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019108752\&doi=10.1109\%2fICMCS.2016.7905544\&partnerID=40\&md5=2acc848761420a801ab6567757203ac4},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R.}
}
