The ant colony optimization meta-heuristic
New ideas in optimization
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ant colony optimization approach for the multidimensional knapsack problem
Journal of Heuristics
Solving the multi-dimensional multi-choice Knapsack problem with the help of ants
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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The ant colony optimization (ACO) algorithms are being applied successfully to diverse heavily constrained problems: traveling salesman problem, quadratic assignment problem. Early applications of ACO algorithms have been mainly concerned with solving ordering problems. In this paper, the principles of the ACO algorithm are applied to the multiple knapsack problem (MKP). In the first part of the paper we explain the basic principles of ACO algorithm. In the second part of the paper we propose different types of heuristic information and we compare the obtained results.