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Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems

Segredo, Eduardo; Paechter, Ben; Hart, Emma; Gonz�alez-Vila, Carlos Ignacio

Authors

Eduardo Segredo

Carlos Ignacio Gonz�alez-Vila



Abstract

In order to address the difficult issue of parameter setting within a diversity-based Multi-objective Evolutionary Algorithm (MOEA), we recently proposed a hybrid control scheme based on both Fuzzy Logic Controllers (FLCs) and Hyper-heuristics (HHs). The method simultaneously adapts both symbolic and numeric parameters and was shown to be effective when controlling a diversity-based MOEA applied to a range of benchmark problems. Here, we show that the hybrid control scheme generalises to other meta-heuristics by using it to adapt several parameters of a diversity-based multi-objective Memetic Algorithm (MA) applied to a Frequency Assignment Problem (FAP). Using real-world instances of the FAP, we demonstrate that our proposed parameter control method outperforms parameter tuning of the MA. The results provide new evidence that the method can be successfully applied to significantly more complex problems than the benchmarks previously tested.

Citation

Segredo, E., Paechter, B., Hart, E., & Gonz´alez-Vila, C. I. (2016). Hybrid parameter control approach applied to a diversity-based multi-objective Memetic Algorithm for frequency assignment problems. In 2016 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2016.7743969

Conference Name IEEE World Congress on Computational Intelligence
Start Date Jul 24, 2016
End Date Jul 29, 2016
Acceptance Date Mar 16, 2016
Online Publication Date Nov 21, 2016
Publication Date Nov 21, 2016
Deposit Date May 2, 2016
Publicly Available Date Nov 21, 2016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Book Title 2016 IEEE Congress on Evolutionary Computation (CEC)
ISBN 978-1-5090-0623-6
DOI https://doi.org/10.1109/CEC.2016.7743969
Keywords Diversity-based multi-objective evolutionary algorithms; evolutionary algorithms; frequency assignment problems; fuzzy logic controllers; hyper-heuristics;
Public URL http://researchrepository.napier.ac.uk/id/eprint/9992

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