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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
León, C., Miranda, G., & Segura, C. (2009). Hyperheuristics for a dynamic-mapped multi-objective Island-based model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5518 LNCS, pp. 41–49). https://doi.org/10.1007/978-3-642-02481-8_7
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