Abstract
This work presents a new parallel model for the solution of multi-objective optimization problems. The model is based on the cooperation of a set of evolutionary algorithms. 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. The proposed model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The hyperheuristic is guided by the measurement of the hypervolume achieved by different optimization methods. The model grants more computational resources to those schemes that show a more promising behaviour. The computational results obtained for some tests available in the literature demonstrate the validity of the proposed model.
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León, C., Miranda, G., Segredo, E., Segura, C. (2009). Parallel Hypervolume-Guided Hyperheuristic for Adapting the Multi-objective Evolutionary Island Model. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_22
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DOI: https://doi.org/10.1007/978-3-642-03211-0_22
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