Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Clustering for the design of SONET rings in interoffice telecommunications
Management Science
SIAM Journal on Discrete Mathematics
Designing tributary networks with multiple ring families
Computers and Operations Research
Fiber Network Service Survivability
Fiber Network Service Survivability
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Topological network design for SONET ring architecture
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A geographic perspective on commercial internet survivability
Telematics and Informatics
On stacking bi-directional self-healing-rings on a conduit ring
Computers and Industrial Engineering
An Evolutionary Design Algorithm for Ring-based SDH optical core networks
BT Technology Journal
Metaheuristics for optimization problems in computer communications
Computer Communications
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Particle swarm optimisation for the design of two-connected networks with bounded rings
International Journal of High Performance Systems Architecture
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Ring structures in telecommunications are taking onincreasing importance because of their “self-healing” properties.We consider a ring design problem in which several stacked self-healingrings (SHRs) follow the same route, and, thus, pass through the sameset of nodes. Traffic can be exchanged among these stacked rings at adesignated hub node. Each non-hub node may be connected to multiplerings. It is necessary to determine to which rings each node shouldbe connected, and how traffic should be routed on the rings. Theobjective is to optimize the tradeoff between the costs forconnecting nodes to rings and the costs for routing demand onmultiple rings. We describe a genetic algorithm that finds heuristicsolutions for this problem. The initial generation of solutionsincludes randomly-generated solutions, complemented by “seed”solutions obtained by applying a greedy randomized adaptive searchprocedure (GRASP) to two related problems. Subsequent generations arecreated by recombining pairs of “parent” solutions. Computationalexperiments compare the genetic algorithm with a commercial integerprogramming package.