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Terminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time. | 231.06 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Terminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time.
Description
International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)
Keywords
Terminal Assignment Problem Genetic Algorithm Local Search Algorithm
Pedagogical Context
Citation
Bernardino, E.M., Bernardino, A.M., Sánchez-Pérez, J.M., Gómez-Pulido, J.A., Vega-Rodríguez, M.A. (2009). Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_27.
Publisher
Springer Berlin Heidelberg
CC License
Without CC licence