Abstract
This paper proposes a Binary Ant System (BAS), a new Ant Colony Optimization applied to multidimensional knapsack problem (MKP). In BAS, artificial ants construct the solutions by selecting either 0 or 1 at every bit stochastically biased by the pheromone level. For ease of implementation, the pheromone is designed specially to directly represent the probability of selection. Experimental results show the advantage of BAS over other ACO based algorithms. The ability of BAS in finding the optimal solutions of various benchmarks indicates its potential in dealing with large size MKP instances.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kong, M., Tian, P. (2006). A New Ant Colony Optimization Applied for the Multidimensional Knapsack Problem. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_19
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DOI: https://doi.org/10.1007/11903697_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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