@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 {Moumen20181,
	title = {An NLP based text-to-speech synthesizer for Moroccan Arabic},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-5},
	doi = {10.1109/CloudTech.2017.8284745},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046676982\&doi=10.1109\%2fCloudTech.2017.8284745\&partnerID=40\&md5=9ca745be5c63a00cda019ef8b0230a9d},
	author = {Moumen, R. and Chiheb, R. and Faizi, R. and Afia, A.E.}
}
