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A new ant colony optimization algorithm for the multidimensional Knapsack problem
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DOI：10.1016/j.cor.2006.12.029.png)
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Abstract
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The paper proposes a new ant colony optimization (ACO) approach, called binary ant system (BAS), to multidimensional Knapsack problem (MKP). Different from other ACO-based algorithms applied to MKP, BAS uses a pheromone laying method specially designed for the binary solution structure, and allows the generation of infeasible solutions in the solution construction procedure. A problem specific repair operator is incorporated to repair the infeasible solutions generated in every iteration. Pheromone update rule is designed in such a way that pheromone on the paths can be directly regarded as selecting probability. To avoid premature convergence, the pheromone re-initialization and different pheromone intensification strategy depending on the convergence status of the algorithm are incorporated. Experimental results show the advantages of BAS over other ACO-based approaches for the benchmark problems selected from OR library. (C) 2007 Elsevier Ltd. All rights reserved.
Keywords:
ant colony optimization
binary ant system
combinatorial optimization
multidimensional Knapsack problem
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