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Distributed differential evolution with explorative–exploitative population families

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Abstract

This paper proposes a novel distributed differential evolution algorithm, namely Distributed Differential Evolution with Explorative–Exploitative Population Families (DDE-EEPF). In DDE-EEPF the sub-populations are grouped into two families. Sub-populations belonging to the first family have constant population size, are arranged according to a ring topology and employ a migration mechanism acting on the individuals with the best performance. This first family of sub-populations has the role of exploring the decision space and constituting an external evolutionary framework. The second family is composed of sub-populations with a dynamic population size: the size is progressively reduced. The sub-populations belonging to the second family are highly exploitative and are supposed to quickly detect solutions with a high performance. The solutions generated by the second family then migrate to the first family. In order to verify its viability and effectiveness, the DDE-EEPF has been run on a set of various test problems and compared to four distributed differential evolution algorithms. Numerical results show that the proposed algorithm is efficient for most of the analyzed problems, and outperforms, on average, all the other algorithms considered in this study.

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Acknowledgments

This research is supported by the Academy of Finland, Akatemiatutkija 00853, Algorithmic Design Issues in Memetic Computing.

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  1. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), 40014, Jyväskylä, Finland

    Matthieu Weber, Ferrante Neri & Ville Tirronen

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Correspondence to Ferrante Neri.

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Weber, M., Neri, F. & Tirronen, V. Distributed differential evolution with explorative–exploitative population families. Genet Program Evolvable Mach 10, 343–371 (2009). https://doi.org/10.1007/s10710-009-9089-y

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