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Published in:
2005 | OriginalPaper | Chapter
A Binary Ant Colony Optimization for the Unconstrained Function Optimization Problem
Authors : Min Kong, Peng Tian
Published in: Computational Intelligence and Security
Publisher: Springer Berlin Heidelberg
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This paper proposes a Binary Ant System (BAS), a binary version of the hyper-cube frame for Ant Colony Optimization applied to unconstrained function optimization problem. In BAS, artificial ants construct the solutions by selecting either 0 or 1 at every bit stochastically biased by the pheromone level. For ease of implementation, the pheromone value is designed specially to directly represent the probability of selection. Principal settings of the parameters are analyzed and some methods to escape local optima, such as local search and pheromone re-initialization are incorporated into the proposed algorithm. Experimental results show that the BAS is able to find very good results for the unconstrained function optimization problems of different characteristics.