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A Three-Strategy Based Differential Evolution Algorithm for Constrained Optimization

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Neural Information Processing. Theory and Algorithms (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6443))

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Abstract

Constrained Optimization is one of the most active research areas in the computer science, operation research and optimization fields. The Differential Evolution (DE) algorithm is widely used for solving continuous optimization problems. However, no single DE algorithm performs consistently over a range of Constrained Optimization Problems (COPs). In this research, we propose a Self-Adaptive Operator Mix Differential Evolution algorithm, indicated as SAOMDE, for solving a variety of COPs. SAOMDE utilizes the strengths of three well-known DE variants through an adaptive learning process. SAOMDE is tested by solving 13 test problems. The results showed that SAOMDE is not only superior to three single mutation based DE, but also better than the stateof- the-art algorithms.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales at ADFA (UNSW@ADFA), Canberra, 2600, Australia

    Saber M. Elsayed, Ruhul A. Sarker & Daryl L. Essam

Authors
  1. Saber M. Elsayed
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  2. Ruhul A. Sarker
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  3. Daryl L. Essam
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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© 2010 Springer-Verlag Berlin Heidelberg

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Elsayed, S.M., Sarker, R.A., Essam, D.L. (2010). A Three-Strategy Based Differential Evolution Algorithm for Constrained Optimization. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_71

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  • DOI: https://doi.org/10.1007/978-3-642-17537-4_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17536-7

  • Online ISBN: 978-3-642-17537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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