The ant colony optimization meta-heuristic
New ideas in optimization
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Future Generation Computer Systems
Wideband CDMA For Third Generation Mobile Communications: Universal Personal Communications
Wideband CDMA For Third Generation Mobile Communications: Universal Personal Communications
D-Ants: savings based ants divide and conquer the vehicle routing problem
Computers and Operations Research
Cost-optimal topology planning of hierarchical access networks
Computers and Operations Research
Savings based ant colony optimization for the capacitated minimum spanning tree problem
Computers and Operations Research
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Grenade Explosion Method-A novel tool for optimization of multimodal functions
Applied Soft Computing
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This paper studies the problem of planning UMTS (Universal Mobile Telecommunication System) access network. The aim is to determine the optimal number and location of radio network controllers (RNCs) and to find the connections of minimal cost between RNCs and radio base stations (RBSs) satisfying all the topological constraints. As the problem is NP-hard we propose a hybrid ant colony optimization (ACO) algorithm to tackle it heuristically. The main characteristic of the ACO algorithm is to perturb a saving-based greedy heuristic in its solution construction. We then use decomposition ants (D-ants) to enhance the efficiency of the algorithm. This is achieved by decomposing the master problem and solving only the much smaller sub-problems resulting from decomposition. Comparing with the previous results we will demonstrate through a number of test cases that our algorithms improve best previous results.