scholarly journals A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification

2019 ◽  
Vol 148 ◽  
pp. 512-521 ◽  
Author(s):  
Abdellatif El Afia ◽  
Mohamed Lalaoui ◽  
Raddouane Chiheb
Keyword(s):  
Simulated Annealing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Simulated Annealing Algorithm ◽  
Hidden Markov ◽  
Model State ◽  
State Classification ◽  
Annealing Algorithm

scholarly journals A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

2018 ◽  
Vol 8 (1) ◽  
pp. 291 ◽  
Author(s):  
Mohamed Lalaoui ◽  
Abdellatif El Afia ◽  
Raddouane Chiheb
Keyword(s):  
Simulated Annealing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Simulated Annealing Algorithm ◽  
Learning Algorithm ◽  
Hidden Markov ◽  
Main Idea ◽  
Self Tuning ◽  
Annealing Algorithm ◽  
Search History

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.

scholarly journals Selecting hidden Markov model state number with cross-validated likelihood

2007 ◽  
Vol 23 (4) ◽  
pp. 541-564 ◽  
Author(s):  
Gilles Celeux ◽  
Jean-Baptiste Durand
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Model State
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