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Multiobjective Differential Evolution Algorithm with Self-Adaptive Learning Process

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Recent Advances in Intelligent Engineering Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 378))

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Abstract

This chapter presents an efficient strategy for self-adaptation mechanisms in a multiobjective differential evolution algorithm. The algorithm uses parameters adaptation and operates with two differential evolution schemes. Also, a novel DE mutation scheme combined with a transversal individual idea is introduced to support the convergence rate of the algorithm. The performance of the proposed algorithm, named DEMOSA, is tested on a set of benchmark problems. The numerical results confirm that the proposed algorithm performs considerably better than the one with simple DE scheme in terms of computational cost and quality of the identified nondominated solutions sets.

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Authors and Affiliations

  1. Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wroclaw, Poland

    Andrzej Cichoń & Ewa Szlachcic

Authors
  1. Andrzej Cichoń
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  2. Ewa Szlachcic
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Engineering Systems, Óbuda University, Bécsi út 96/b, H-1034, Budapest, Hungary

    János Fodor

  2. Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, 27 Wybrzeze Wyspianskiego st., 50-372, Wrocław, Poland

    Ryszard Klempous

  3. Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, Instituto Universitario de Ciencias y Tecnologías Cibernéticas, Campus Universitario de Tafira s/n, 35017, Las Palmas de Gran, Canaria, Spain

    Carmen Paz Suárez Araujo

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Cichoń, A., Szlachcic, E. (2012). Multiobjective Differential Evolution Algorithm with Self-Adaptive Learning Process. In: Fodor, J., Klempous, R., Suárez Araujo, C.P. (eds) Recent Advances in Intelligent Engineering Systems. Studies in Computational Intelligence, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23229-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-23229-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23228-2

  • Online ISBN: 978-3-642-23229-9

  • eBook Packages: EngineeringEngineering (R0)

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