Abstract
This paper proposes a Binary Ant Colony Optimization applied to constrained optimization problems with binary solution structure. Due to its simple structure, the convergence status of the proposed algorithm can be monitored through the distribution of pheromone in the solution space, and the probability of solution improvement can be in some way controlled by the maintenance of pheromone. The successful implementations to the binary function optimization problem and the multidimensional knapsack problem indicate the robustness and practicability of the proposed algorithm.
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Kong, M., Tian, P. (2006). Introducing a Binary Ant Colony Optimization. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_44
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DOI: https://doi.org/10.1007/11839088_44
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