Abstract
The Ant Colony Optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application. In order to improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented by adjusting pheromone approach, introducing a disaster operator, and combining the ACO with the saving algorithm and λ-interchange mechanism. Then, the HACO is applied to solve the vehicle routing problem with time windows. By comparing the computational results with the previous literature, it is concluded that the HACO is an effective way to solve combinatorial optimization problems.
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Hu, X., Ding, Q., Wang, Y. (2010). A Hybrid Ant Colony Optimization and Its Application to Vehicle Routing Problem with Time Windows. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15853-7_10
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DOI: https://doi.org/10.1007/978-3-642-15853-7_10
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