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Bare bones differential evolution

Author

Listed:
  • Omran, Mahamed G.H.
  • Engelbrecht, Andries P.
  • Salman, Ayed

Abstract

The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.

Suggested Citation

  • Omran, Mahamed G.H. & Engelbrecht, Andries P. & Salman, Ayed, 2009. "Bare bones differential evolution," European Journal of Operational Research, Elsevier, vol. 196(1), pages 128-139, July.
  • Handle: RePEc:eee:ejores:v:196:y:2009:i:1:p:128-139
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    Citations

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    Cited by:

    1. du Plessis, Mathys C. & Engelbrecht, Andries P., 2012. "Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments," European Journal of Operational Research, Elsevier, vol. 218(1), pages 7-20.
    2. Sotirios K. Goudos & Margot Deruyck & David Plets & Luc Martens & Wout Joseph, 2017. "Optimization of Power Consumption in 4G LTE Networks Using a Novel Barebones Self-adaptive Differential Evolution Algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 66(1), pages 109-120, September.
    3. Piotrowski, Adam P. & Napiorkowski, Jaroslaw J. & Kiczko, Adam, 2012. "Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 33-46.
    4. Narang, Nitin & Dhillon, J.S. & Kothari, D.P., 2012. "Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method," Energy, Elsevier, vol. 47(1), pages 237-252.
    5. Fan, Qinqin & Yan, Xuefeng & Zhang, Yilian, 2018. "Auto-selection mechanism of differential evolution algorithm variants and its application," European Journal of Operational Research, Elsevier, vol. 270(2), pages 636-653.
    6. Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2011. "A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1901-1909.
    7. Zhang, Enze & Wu, Yifei & Chen, Qingwei, 2014. "A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 65-76.

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