Telecommunications network design algorithms
Telecommunications network design algorithms
Computers and Operations Research
Combinatorial optimization
New ideas in optimization
Tabu Search
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Zoom-In Approach to Design SDH Mesh Restorable Networks
Journal of Heuristics
Spare Capacity Planning for Survivable Mesh Networks
NETWORKING '00 Proceedings of the IFIP-TC6 / European Commission International Conference on Broadband Communications, High Performance Networking, and Performance of Communication Networks
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
A tabu search algorithm for the routing and capacity assignment problem in computer networks
Computers and Operations Research
Survivable IP network design with OSPF routing
Networks - Special Issue on Multicommodity Flows and Network Design
A Simulated Annealing approach for mobile location management
Computer Communications
Design of Ant Colony -- based algorithm Ant Route for solve the OSPF problem
CERMA '07 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference
Survivable and delay-guaranteed backbone wireless mesh network design
Journal of Parallel and Distributed Computing
Topological design of survivable IP networks using metaheuristic approaches
QoS-IP'05 Proceedings of the Third international conference on Quality of Service in Multiservice IP Networks
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
WDM optical communication networks: progress and challenges
IEEE Journal on Selected Areas in Communications
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The complete topology design problem of survivable mesh-based transport networks is to address simultaneously design of network topology, working path routing, and spare capacity allocation based on span-restoration. Each constituent problem in the complete design problem could be formulated as an Integer Programming (IP) and is proved to be $\mathcal{NP}$ -hard. Due to a large amount of decision variables and constraints involved in the IP formulation, to solve the problem directly by exact algorithms (e.g. branch-and-bound) would be impractical if not impossible. In this paper, we present a two-level evolutionary approach to address the complete topology design problem. In the low-level, two parameterized greedy heuristics are developed to jointly construct feasible solutions (i.e., closed graph topologies satisfying all the mesh-based network survivable constraints) of the complete problem. Unlike existing "zoom-in"-based heuristics in which subsets of the constraints are considered, the proposed heuristics take all constraints into account. An estimation of distribution algorithm works on the top of the heuristics to tune the control parameters. As a result, optimal solution to the considered problem is more likely to be constructed from the heuristics with the optimal control parameters. The proposed algorithm is evaluated experimentally in comparison with the latest heuristics based on the IP software CPLEX, and the "zoom-in"-based approach on 28 test networks problems. The experimental results demonstrate that the proposed algorithm is more effective in finding high-quality topologies than the IP-based heuristic algorithm in 21 out of 28 test instances with much less computational costs, and performs significantly better than the "zoom-in"-based approach in 19 instances with the same computational costs.