Abstract
Ant Colony Optimization (ACO) is one of the swarm intelligent methods for solving computational problems, especially in finding the optimal paths through graphs. In the past, floating point is widely used to represent the pheromone in ACO, thus requiring large amounts of memory to find the optimal solutions. In this paper, the quantification-based ACS (QACS) is thus proposed to reduce the space complexity. New updating rules of pheromone with no decay parameters are also designed in the proposed QACS for simplifying the updating processing of pheromone. Based on the experimental results of proposed QACS, the convergence rate can be improved with less memory space for solving Traveling Salesman Problem (TSP).
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Zhao, M., Pan, JS., Lin, CW., Yan, L. (2014). Quantification-Based Ant Colony System for TSP. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_36
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DOI: https://doi.org/10.1007/978-3-319-01796-9_36
Publisher Name: Springer, Cham
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