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RESEARCH PRODUCT

An environment based approach for the ant colony convergence

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Abstract Ant colony optimization (ACO) algorithms are a bio inspired solutions which have been very successful in combinatorial problem solving, also known as NP-hard problems, including transportation system optimization. As opposed to exact methods, which could give the best results of a tested problem, this meta-heuristics is based on the stochastic logic but not on theoretical mathematics demonstration (or only on certain well defined applications). According to this, the weak point of this meta-heuristics is his convergence, its termination condition. We can finds many different termination criteria in the scientific literature, yet most of them are costly in resources and unsuitable for practical problems. On the other hand, given the fact that the ACO is a stochastic approach, it seems difficult to decide whether to stop the algorithms in order to have the optimal result of the tested problems. Therefore, the thesis of this paper is to propose an approach based on the environment in order to determine the best termination criteria of the ACO, for an optimized solution.