Future Generation Computer Systems
On the use of genetic algorithms to solve location problems
Computers and Operations Research - Location analysis
Updating ACO Pheromones Using Stochastic Gradient Ascent and Cross-Entropy Methods
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Ant Colony Optimization
IBM Journal of Research and Development
An adaptive search heuristic for the capacitated fixed charge location problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient metaheuristics to solve the intermodal terminal location problem
Computers and Operations Research
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This paper presents two different $\mathcal{MAX-MIN}$ Ant System ($\mathcal{MM}$AS) based algorithms for the Capacitated Fixed Charge Location Problem (CFCLP) which is a discrete facility location problem that consists of selecting a subset of facilities that must completely supply a set of customers at a minimum cost. The first algorithm is concerned with extending and improving existing work primarily by introducing a previously unconsidered local search scheme based on pheromone intensity. Whilst, the second method makes a transformation of the derived $\mathcal{MM}$AS algorithm into the hyper-cube famework in an attempt to improve efficiency and robustness. Computational results for a series of standard benchmark problems are presented and indicate that the proposed methods are capable of deriving optimal solutions for the CFCLP.