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

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