@conference {Afia2019512,
	title = {A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification},
	booktitle = {Procedia Computer Science},
	volume = {148},
	year = {2019},
	note = {cited By 0},
	pages = {512-521},
	doi = {10.1016/j.procs.2019.01.024},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062665945\&doi=10.1016\%2fj.procs.2019.01.024\&partnerID=40\&md5=7a6b9cd9dfff11052107df70b4b828f0},
	author = {Afia, A.E. and Lalaoui, M. and Chiheb, R.}
}
@conference {Lalaoui20181,
	title = {A self-adaptive very fast simulated annealing based on Hidden Markov model},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-8},
	doi = {10.1109/CloudTech.2017.8284698},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046690728\&doi=10.1109\%2fCloudTech.2017.8284698\&partnerID=40\&md5=019d60ddd6cdf20019791974755a6d33},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@article {Lalaoui2018291,
	title = {A self-tuned simulated annealing algorithm using hidden markov model},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {8},
	number = {1},
	year = {2018},
	note = {cited By 0},
	pages = {291-298},
	abstract = {Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one. {\textcopyright} 2018 Institute of Advanced Engineering and Science.},
	doi = {10.11591/ijece.v8i1.pp291-298},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923643\&doi=10.11591\%2fijece.v8i1.pp291-298\&partnerID=40\&md5=804e27501baca7ca4efeeaa3d1ff0ef1},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@article {Lalaoui2018291,
	title = {A self-tuned simulated annealing algorithm using hidden markov model},
	journal = {International Journal of Electrical and Computer Engineering},
	volume = {8},
	number = {1},
	year = {2018},
	pages = {291-298},
	doi = {10.11591/ijece.v8i1.pp291-298},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923643\&doi=10.11591\%2fijece.v8i1.pp291-298\&partnerID=40\&md5=804e27501baca7ca4efeeaa3d1ff0ef1},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
@conference {Lalaoui2017558,
	title = {Hidden Markov Model for a self-learning of Simulated Annealing cooling law},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2017},
	note = {cited By 0},
	pages = {558-563},
	abstract = {The Simulated Annealing (SA) is a stochastic local search algorithm. It is an adaptation of the Metropolis-Hastings Monte Carlo algorithm. SA mimics the annealing process in metallurgy to approximate the global optimum of an optimization problem and uses a temperature parameter to control the search. The efficiency of the simulated annealing algorithm involves the adaptation of the cooling schedule. In this paper, we integrate Hidden Markov Model (HMM) in SA to iteratively predict the best cooling law according to the search history. Experiments performed on many benchmark functions show that our proposed scheme outperforms other SA variants in term of quality of solutions. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ICMCS.2016.7905557},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019141326\&doi=10.1109\%2fICMCS.2016.7905557\&partnerID=40\&md5=68063f3aef36190a9965517a3e628711},
	author = {Lalaoui, M. and El Afia, A. and Chiheb, R.}
}
