Convergence of an annealing algorithm
Mathematical Programming: Series A and B
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computers and Operations Research
Terminal assignment in a communications network using genetic algorithms
CSC '94 Proceedings of the 22nd annual ACM computer science conference on Scaling up : meeting the challenge of complexity in real-world computing applications: meeting the challenge of complexity in real-world computing applications
A genetic algorithm for distributed system topology design
Computers and Industrial Engineering - Collection of papers on Computer-Integrated Manufacturing
A tabu search for the survivable fiber optic communication network design
Computers and Industrial Engineering
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Combinatorics of Network Reliability
The Combinatorics of Network Reliability
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Estimation of all-terminal network reliability using an artificial neural network
Computers and Operations Research
A Genetic Algorithm for Survivable Network Design
Proceedings of the 5th International Conference on Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
Designing resilient networks using a hybrid genetic algorithm approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Particle swarm optimisation for the design of two-connected networks with bounded rings
International Journal of High Performance Systems Architecture
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In many computer communications network design problems, such as those faced by hospitals, universities, research centers, and water distribution systems, the topology is fixed because of geographical and physical constraints or the existence of an existing system. When the topology is known, a reasonable approach to design is to select components among discrete alternatives for links and nodes to maximize reliability subject to cost. This problem is NP-hard with the added complication of a very computationally intensive objective function. This paper compares the performance of three classic metaheuristic procedures for solving large and realistic versions of the problem: hillclimbing, simulated annealing and genetic algorithms. Three alterations that use local search to seed the search or improve solutions during each iteration are also compared. It is shown that employing local search during evolution of the genetic algorithm, a memetic algorithm, yields the best network designs and does so at a reasonable computational cost. Hillclimbing performs well as a quick search for good designs, but cannot identify the most superior designs even when computational effort is equal to the metaheuristics.