Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
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Artificial Life
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Journal of Heuristics
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IEEE Transactions on Evolutionary Computation
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ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
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Computers and Operations Research
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CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
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Applied Soft Computing
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AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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Expert Systems with Applications: An International Journal
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ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
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Expert Systems with Applications: An International Journal
Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
Journal of Global Optimization
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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.