Abstract
This work presents a set of improvements and a performance analysis for a previously designed multi-objective optimisation parallel model. The model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach in order to grant more computational resources to those schemes that show a more promising behaviour. The main aim is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. A contribution-based hyperheuristic previously presented in the literature is compared with a novel hypervolume-based hyperheuristic. The computational results obtained for some tests available in the literature demonstrate the superiority of the hypervolume-based hyperheuristic when compared to the contribution-based hyperheuristic and to other standard parallel models.
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León, C., Miranda, G., Segura, C. (2009). Hyperheuristics for a Dynamic-Mapped Multi-Objective Island-Based Model. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_7
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DOI: https://doi.org/10.1007/978-3-642-02481-8_7
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