Authors: Jenn-Long Liu, Chao-Wei Chou, Chia-Mei Chen
Addresses: Department of Information Management, I-Shou University, Kaohsiung 840, Taiwan, ROC. ' Department of Information Management, I-Shou University, Kaohsiung 840, Taiwan, ROC. ' Department of Information Management, National Sun Yat-Sen University, Kaohsiung 804, Taiwan, ROC
Abstract: This work proposes an enhanced Multi-Objective Genetic Algorithm (enhanced MOGA), which includes non-dominated sorting, crowded distance sorting, binary tournament selection, extended intermediate crossover and non-uniform mutation operators, for optimising mobile base station placement. The performance of the enhanced MOGA and Deb et al.|s NSGA-II are compared by applying these two codes to benchmark problem computations. Moreover, three cases of mobile base station placement, which include homogeneous and heterogeneous transmitters located in the placement regions, are studied by the present enhanced MOGA. The non-dominated solutions are presented in terms of realistic cellular placement with handover constraint in mobile network communications.
Keywords: multi-objective genetic algorithms; enhanced MOGA; mobile base station placement; homogeneous transmitters; heterogeneous transmitters; non-dominated sorting; crowded distance sorting; binary tournament selection; extended intermediate crossover; non-uniform mutation operators; non-dominated sorting genetic algorithm-II; NSGA-II; cellular placements; handover constraints; mobile phones; communication networks; cell phones; data mining; business intelligence.
International Journal of Business Intelligence and Data Mining, 2010 Vol.5 No.1, pp.19 - 42
Published online: 14 Dec 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article