Hybrid Population-based Incremental Learning to Assign Terminals to Concentrators

In the last decade, we have seen a significant growth in communication networks. In centralised communication networks, a central computer serves several terminals or workstations. In large networks, some concentrators are used to increase the network efficiency. A collection of terminals is connected to a concentrator and each concentrator is connected to the central computer. In this paper we propose a Hybrid Population-based Incremental Learning (HPBIL) to assign terminals to concentrators. We use this algorithm to determine the minimum cost to form a network by connecting a given collection of terminals to a given collection of concentrators. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.

[1]  Roger L. Wainwright,et al.  Terminal assignment in a communications network using genetic algorithms , 1994, CSC '94.

[2]  Miguel A. Vega-Rodríguez,et al.  A Hybrid Differential Evolution Algorithm for Solving the Terminal Assignment Problem , 2009, IWANN.

[3]  Miguel A. Vega-Rodríguez,et al.  A Hybrid Scatter Search algorithm to assign terminals to concentrators , 2010, IEEE Congress on Evolutionary Computation.

[4]  Sami Khuri,et al.  Heuristic algorithms for the terminal assignment problem , 1997, SAC '97.

[5]  Xin Yao,et al.  A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Shumeet Baluja,et al.  Genetic Algorithms and Explicit Search Statistics , 1996, NIPS.

[7]  Chengjian Wei,et al.  A new population-based incremental learning method for the traveling salesman problem , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  Xin Yao,et al.  Hybrid Evolutionary Approaches to Terminal Assignment in Communications Networks , 2005 .

[9]  Miguel A. Vega-Rodríguez,et al.  Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm , 2008, DCAI.

[10]  Rahul Sukthankar,et al.  Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms , 1997 .

[11]  Miguel A. Vega-Rodríguez,et al.  A Hybrid Ant Colony Optimization Algorithm for Solving the Terminal Assignment Problem , 2009, IJCCI.

[12]  Xin Yao,et al.  Non-standard cost terminal assignment problems using tabu search approach , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[13]  M.A. Vega-Rodriguez,et al.  Population-Based Incremental Learning to Solve the FAP Problem , 2008, 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences.

[14]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.