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A Differential Evolution Algorithm with a Variable Neighborhood Search for Constrained Function Optimization

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Adaptation and Hybridization in Computational Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 18))

Abstract

In this paper, a differential evolution algorithm based on a variable neighborhood search algorithm (DE_VNS) is proposed in order to solve the constrained real-parameter optimization problems. The performance of DE algorithm depends on the mutation strategies, crossover operators and control parameters. As a result, a DE_VNS algorithm that can employ multiple mutation operators in its VNS loops is proposed in order to further enhance the solution quality. We also present an idea of injecting some good dimensional values to the trial individual through the injection procedure. In addition, we also present a diversification procedure that is based on the inversion of the target individuals and injection of some good dimensional values from promising areas in the population by tournament selection. The computational results show that the simple DE_VNS algorithm was very competitive to some of the best performing algorithms from the literature.

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Author information

Authors and Affiliations

  1. Industrial Engineering Department, Yasar University, Selcuk Yasar Campus, Izmir, Turkey

    M. Fatih Tasgetiren, Sel Ozcan & Damla Kizilay

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    P. N. Suganthan

Authors
  1. M. Fatih Tasgetiren
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  2. P. N. Suganthan
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  3. Sel Ozcan
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  4. Damla Kizilay
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Correspondence to M. Fatih Tasgetiren .

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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

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Tasgetiren, M.F., Suganthan, P.N., Ozcan, S., Kizilay, D. (2015). A Differential Evolution Algorithm with a Variable Neighborhood Search for Constrained Function Optimization. In: Fister, I., Fister Jr., I. (eds) Adaptation and Hybridization in Computational Intelligence. Adaptation, Learning, and Optimization, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-14400-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-14400-9_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14399-6

  • Online ISBN: 978-3-319-14400-9

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