A Population Based Approach for ACO (2002) [14 citations — 4 self]
by
Michael Guntsch
,
Martin Middendorf
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@MISC{Guntsch02apopulation,
author = {Michael Guntsch and Martin Middendorf},
title = {A Population Based Approach for ACO},
year = {2002}
}
author = {Michael Guntsch and Martin Middendorf},
title = {A Population Based Approach for ACO},
year = {2002}
}
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Abstract:
A population based ACO (Ant Colony Optimization) algorithm is proposed where (nearly) all pheromone information corresponds to solutions that are members of the actual population. Advantages of the population based approach are that it seems promising for solving dynamic optimization problems, its nite state space and the chances it oers for designing new metaheuristics. We compare the behavior of the new approach to the standard ACO approach for several instances of the TSP and the QAP problem. The results show that the new approach is competitive.
