Guided local search as a network planning algorithm that incorporates uncertain traffic demands |
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Affiliation: | 1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki,54124, Greece;2. Computer Architecture and Communication Area, University Carlos III, Avda. Universidad, 30,Madrid,28911, Spain;1. School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China;2. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, PR China;3. Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom |
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Abstract: | 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. |
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