Guided local search as a network planning algorithm that incorporates uncertain traffic demands
Computer Networks: The International Journal of Computer and Telecommunications Networking
Applications of genetic algorithms to optimal multilevel design of MPLS-based networks
Computer Communications
Multiobjective network design for realistic traffic models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Soft computing in engineering design - A review
Advanced Engineering Informatics
A self-learning optimization technique for topology design of computer networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Nonlinear network optimization: an embedding vector space approach
IEEE Transactions on Evolutionary Computation
Improving a local search technique for network optimization using inexact forecasts
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Proceedings of the 45th Annual Simulation Symposium
Optimizing virtual private network design using a new heuristic optimization method
ISRN Communications and Networking
Meta-heuristic algorithms for optimized network flow wavelet-based image coding
Applied Soft Computing
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This paper deals with the application of evolutionary computation to telecommunication network design. Design of a two-layer network is considered, where the upper-layer (UL) network uses resources of the lower-layer (LL) network. UL links determine demands for the LL and are implemented using LL paths (admissible paths). Within a fixed LL network topology, given the demands and admissible paths, we aim to find the LL link capacities for implementing the UL links, minimizing the cost of the LL. Robust design issues are also taken into consideration, allowing for failure of a certain part of the LL and postulating that, after some re-allocation in the LL, demands are still realized to an assumed extent. An algorithm based on an evolutionary technique is introduced, with problem-specific genetic operators to improve computing efficiency. A theoretical study of properties of the operators is made and several experiments are performed to tune the parameters of the algorithm. Finally, its performance is compared with other design techniques, including integer programming