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A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test

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Abstract

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue.

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Acknowledgments

This work was supported by the Madrid Regional Education Ministry and the European Social Fund, financed by the Spanish Ministry of Science TIN2007-67148 and supported by the Cajal Blue Brain Project. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Centro de Supercomputación y Visualización de Madrid (CeSViMa) and the Spanish Supercomputing Network.

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  1. Department of Computer Systems Architecture and Technology, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain

    Antonio LaTorre, Santiago Muelas & José-María Peña

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  1. Antonio LaTorre
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  2. Santiago Muelas
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  3. José-María Peña
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Correspondence to Antonio LaTorre.

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LaTorre, A., Muelas, S. & Peña, JM. A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test. Soft Comput 15, 2187–2199 (2011). https://doi.org/10.1007/s00500-010-0646-3

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