Fast approximation algorithms for multicommodity flow problems
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Finding a feasible course schedule using Tabu search
Discrete Applied Mathematics - Special issue: Timetabling and chromatic scheduling
Telecommunications network design algorithms
Telecommunications network design algorithms
Modern heuristic techniques for combinatorial problems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
A simple local-control approximation algorithm for multicommodity flow
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
Applying an evolutionary algorithm to telecommunication networkdesign
IEEE Transactions on Evolutionary Computation
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This paper presents an evolutionary computation approach to optimise the design of communication networks where traffic forecasts are uncertain. The work utilises Fast Local Search (FLS), which is an improved hill climbing method and uses Guided Local Search (GLS) to escape from local optima and to distribute the effort throughout the solution space. The only parameter that needs to be tuned in GLS is called the regularization parameter lambda (λ). This parameter represents the degree up to which constraints on the features in the optimization problem are going to affect the outcome of the local search. To fine-tune this parameter, a series of evaluations were performed in several network scenarios to investigate the application towards network planning. Two types of performance criteria were evaluated: computation time and overall cost. Previous work by the authors has introduced the technique without fully investigating the sensitivity of λ on the performance. The significant result from this work is to show that the computational performance is relatively insensitive to the value of λ and a good value for the problem type specified is given.