Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Heuristic Approach for Antenna Positioning in Cellular Networks
Journal of Heuristics
Niching and Elitist Models for MOGAs
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
Mobile Networks and Applications
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Computational Optimization and Applications
Implementation of scatter search for multi-objective optimization: a comparative study
Computational Optimization and Applications
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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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.