A fuzzy ant colony optimization algorithm for topology design of distributed local area networks

Ant colony optimization (ACO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is network topology design of distributed local area networks (DLANs). The problem requires simultaneous optimization of a number of objectives, such as monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective ant colony optimization algorithm to efficiently solve the DLAN topology design problem. The multi-objective aspect of the problem is handled by incorporating fuzzy logic in the ACO algorithm. The performance of fuzzy ACO is evaluated through comparison with a fuzzy simulated annealing algorithm. Empirical results suggest that the fuzzy ACO produces results of equal quality when compared with a fuzzy simulated annealing algorithm.

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