Telecommunications network design algorithms
Telecommunications network design algorithms
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
Topology generation based on network design heuristics
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
GaMa: An Evolutionary Algorithmic Approach for the Design of Mesh-Based Radio Access Networks
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
MPLS Network Topology Design Using Genetic Algorithms
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
The equation for response to selection and its use for prediction
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Applying an evolutionary algorithm to telecommunication networkdesign
IEEE Transactions on Evolutionary Computation
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Topology design of computer networks is a constrained optimization problem for which exact solution approaches do not scale well. This paper introduces a self-learning, non-greedy optimization technique for network topology design. It generates new solutions based on the merit of the preceding ones. This is achieved by maintaining a solution library for all the variables. Based on certain heuristics, the library is updated after each set of generated solutions. The algorithm has been applied to a MPLS-based IP network design problem. The network consists of a set of Label Edge Routers (LERs) routing the total traffic through a set of Label Switching Routers (LSRs) and interconnecting links. The design task consists of -- 1) assignment of user terminals to LERs; 2) placement of LERs; and 3) selection of the actually installed LSRs and their links, while distributing the traffic over the network. Results show that our techniques attain the optimal solution, as given by GNU solver - lp_solve, effectively with minimum computational burden.