期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 23, 期 5, 页码 676-688出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2009.09.011
关键词
Multilevel thresholding; Image segmentation; Genetic algorithm; Particle swarm optimization; Differential evolution; Ant colony optimization; Simulated annealing; Tabu search
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. (C) 2009 Elsevier Ltd. All rights reserved.
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