Abstract
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.
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Lucio, G.F., Reed, M.J., Henning, I.D. (2005). Improving a Local Search Technique for Network Optimization Using Inexact Forecasts. In: Lorenz, P., Dini, P. (eds) Networking - ICN 2005. ICN 2005. Lecture Notes in Computer Science, vol 3420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31956-6_75
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DOI: https://doi.org/10.1007/978-3-540-31956-6_75
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