Probabilistic Model of Ant Colony Optimization for Multiple Knapsack Problem

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.

• Type: Conference
• Year: 2007
• Where: LSSC
• Authors: Stefka Fidanova
• Keywords: ACO Algorithms, Ant Colony Optimization, Applied Computing, LSSC 2007, Probabilistic Model