Guided local search as a network planning algorithm that incorporates uncertain traffic demands
A search based heuristic for the optimisation of communication networks where traffic forecasts are uncertain and the problem is NP-complete is presented. While algorithms such as genetic algorithms (GA) and simulated annealing (SA) are often used for this class of problem, this work applies a combination of newer optimisation techniques specifically: fast local search (FLS) as an improved hill climbing method and guided local search (GLS) to allow escape from local minima. The GLS+FLS combination is compared with an optimised GA and SA approaches. It is found that in terms of implementation, the parameterisation of the GLS+FLS technique is significantly simpler than that for a GA and SA. Also, the self-regularisation feature of the GLS+FLS approach provides a distinctive advantage over the other techniques which require manual parameterisation. To compare numerical performance, the three techniques were tested over a number of network sets varying in size, number of switch circuit demands (network bandwidth demands) and levels of uncertainties on the switch circuit demands. The results show that the GLS+FLS outperforms the GA and SA techniques in terms of both solution quality and optimisation speed but even more importantly GLS+FLS has significantly reduced parameterisation time.