Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Hierarchical parallel approach for GSM mobile network design
Journal of Parallel and Distributed Computing
A Differential Evolution Based Algorithm to Optimize the Radio Network Design Problem
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Do additional objectives make a problem harder?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
A multi-objective evolutionary approach for the antenna positioning problem
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Antenna Positioning Problem (APP) is an NP-Complete Optimisation Problem which arises in the telecommunication field. Its aim is to identify the infrastructures required to establish a wireless network. A well-known mono-objective version of the problem has been used. The best-known approach to tackle such a version is a problem-dependent strategy. However, other methods which minimise the usage of problem-dependent information have also been defined. Specifically, multi-objectivisation has provided solutions of similar quality than problem-dependent strategies. However, it requires a larger amount of time to converge to high-quality solutions. The main aim of the present work has been the decrease of the time invested in solving app with multi-objectivisation. For this purpose, a parallel island-based model has been applied to two app instances. In order to check the robustness of the approach, several migration stages have been tested. In addition, a scalability analysis using the best-behaved migration stage has been performed. Computational results have demonstrated the validity of the proposal.