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A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization

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Contemporary Computing (IC3 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 40))

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Abstract

Differential Evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of real valued, multi modal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution (MDE), a modification to DE that enhances the convergence rate without compromising with the solution quality. In Modified differential evolution (MDE) algorithm, if an individual fails in continuation to improve its performance to a specified number of times then new point is generated using Cauchy mutation. MDE on a test bed of functions is compared with original DE. It is found that MDE requires less computational effort to locate global optimal solution.

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Author information

Authors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Saharanpur, 247001, India

    Musrrat Ali, Millie Pant & Ved Pal Singh

Authors
  1. Musrrat Ali
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  2. Millie Pant
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  3. Ved Pal Singh
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Editor information

Editors and Affiliations

  1. Dept. of Computer Sciences, University of Florida, Gainesville, FL, 32611, USA

    Sanjay Ranka

  2. Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA

    Srinivas Aluru

  3. Grid Computing and Distributed Systems Laboratory and, NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

    Rajkumar Buyya

  4. Department of Computer Science, National Tsing Hua University, Taiwan

    Yeh-Ching Chung

  5. Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, 71272, USA

    Sumeet Dua  & Vir V. Phoha  & 

  6. Department of Computer Sciences, Purdue University, W. Lafayette, IN, 47907, USA

    Ananth Grama

  7. Arizona State University, Tempe, AZ, 85281, USA

    Sandeep K. S. Gupta

  8. Computer Science and Engineering Department, Indian Institute of Technology Kharagpur, 721 302, WB, India

    Rajeev Kumar

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© 2009 Springer-Verlag Berlin Heidelberg

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Ali, M., Pant, M., Singh, V.P. (2009). A Modified Differential Evolution Algorithm with Cauchy Mutation for Global Optimization. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-03547-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03546-3

  • Online ISBN: 978-3-642-03547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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