Hidden Markov Model for a self-learning of Simulated Annealing cooling law

TitreHidden Markov Model for a self-learning of Simulated Annealing cooling law
Publication TypeConference Paper
Year of Publication2017
AuthorsLalaoui, M, A. Afia, E, Chiheb, R
Conference NameInternational Conference on Multimedia Computing and Systems -Proceedings
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. © 2016 IEEE.

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85019141326&doi=10.1109%2fICMCS.2016.7905557&partnerID=40&md5=68063f3aef36190a9965517a3e628711
DOI10.1109/ICMCS.2016.7905557
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