Abstract
An ant colony optimization algorithm using a library of partial solutions for knowledge incorporation from previous iterations is introduced. Initially, classical ant colony optimization algorithm runs for a small number of iterations and the library of partial solutions is initialized. In this library, variable size solution segments from a number of elite solutions are stored and each segment is associated with its parent’s objective function value. There is no particular distribution of ants in the problem space and the starting point for an ant is the initial point of the segment it starts with. In order to construct a solution, a particular ant retrieves a segment from the library based on its goodness and completes the rest of the solution. Constructed solutions are also used to update the memory. The proposed approach is used for the solution of TSP and QAP for which the obtained results demonstrate that both the speed and solution quality are improved compared to conventional ACO algorithms.
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Acan, A. (2004). An External Memory Implementation in Ant Colony Optimization. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_7
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DOI: https://doi.org/10.1007/978-3-540-28646-2_7
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