Abstract
Ant colony algorithms analogize the social behaviour of ant colonies, they are a class of meta-heuristics which are inspired from the behavior of real ants. It was applied successfully to the well-known traveling salesman problem and other hard combinational optimization problems. In order to apply it to the classical 0/1 knapsack problem, this paper compares the difference between the traveling salesman problem and the 0/1 knapsack problem and adapts the ant colony optimization (ACO) model to meet researches' purpose. At the same time, the parameters in ACO model are modified accordingly. The experiments based on improved ant colony algorithms show the robustness and the potential power of this kind of meta-heuristic algorithm.
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