Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Solving Multiple Knapsack Problems by Cutting Planes
SIAM Journal on Optimization
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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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 the probabilistic model in model-based search as ACO. We present the effect of four different probabilistic models for ACO algorithms 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 probabilistic model to quality of the solutions.