A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

Authors:
Kamal Hammouche;Moussa Diaf;Patrick Siarry
Affiliations:
Université Mouloud Mammeri, Département Automatique, Tizi-Ouzou, Algeria;Université Mouloud Mammeri, Département Automatique, Tizi-Ouzou, Algeria;Laboratoire Images, Signaux et Systèmes Intelligents (LiSSi, EA 3956), Université Paris XII Val de Marne, 61 avenue du Général de Gaulle, 94010 Créteil, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

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Abstract

The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly.