Research Output

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

  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.

  • Date:

    21 November 2016

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/CEC.2016.7743969

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    621.381 Electronics

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)doi:10.1109/CEC.2016.7743969. ISBN 978-1-5090-0623-6

Authors

Copyright

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Keywords

Diversity-based multi-objective evolutionary algorithms; evolutionary algorithms; frequency assignment problems; fuzzy logic controllers; hyper-heuristics;

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