On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Stochastic evolution: a fast effective heuristic for some generic layout problems
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Topological design of local-area networks using genetic algorithms
IEEE/ACM Transactions on Networking (TON)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
F-LQE: a fuzzy link quality estimator for wireless sensor networks
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
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Topology design of enterprise networks is a hard combinatorial optimization problem. It has numerous constraints, several objectives, and a very noisy solution space. Besides the NP-hard nature of this problem, many of the performance metrics of the network can only be estimated, given their dependence on many of the dynamic aspects of the network, e.g., routing and number and type of traffic sources. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present a fuzzy evolutionary hybrid metaheuristic for network topology design. This approach is dominance preserving and scales well with larger problem instances and a larger number of objective criteria. Experimental results are provided.