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 finite state space and the chances it offers 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. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Guntsch, M., & Middendorf, M. (2002). A population based approach for ACO. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2279 LNCS, pp. 72–81). Springer Verlag. https://doi.org/10.1007/3-540-46004-7_8
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