Ant Colony Optimization
A Branch-and-Bound Algorithm for Concave Network Flow Problems
Journal of Global Optimization
Adaptive dynamic cost updating procedure for solving fixed charge network flow problems
Computational Optimization and Applications
Ant colony optimization for the cell assignment problem in PCS networks
Computers and Operations Research
Ant colony optimization algorithm to solve for the transportation problem of cross-docking network
Computers and Industrial Engineering
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Concave minimum cost network flow problems solved with a colony of ants
Journal of Heuristics
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In this work we address the Singe-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. Given that this problem is of a combinatorial nature and also that the total costs are nonlinear, we propose a hybrid heuristic to solve it. In this type of algorithms one usually tries to manage two conflicting aspects of searching behaviour: exploration, the algorithm's ability to search broadly through the search space; and exploitation, the algorithm ability to search locally around good solutions that have been found previously. In our case, we use an Ant Colony Optimization algorithm to mainly deal with the exploration, and a Local Search algorithm to cope with the exploitation of the search space. Our method proves to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics and our algorithm was able to improve upon their results, both in terms of computing time and solution quality.