Ant Colony Optimization for Multiple Knapsack Problem and Model Bias

The Ant Colony Optimization (ACO) algorithms are being applied successfully to a wide range of problems. ACO algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems (COPs). In this paper we investigate the influence of model bias in model-based search as ACO. We present the effect of two different pheromone models for ACO algorithm to tackle the Multiple Knapsack Problem (MKP). The MKP is a subset problem and can be seen as a general model for any kind of binary problems with positive coefficients. The results show the importance of the pheromone model to quality of the solutions.

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