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Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms

Author

Listed:
  • Mahamed Omran
  • Salah al-Sharhan
  • Ayed Salman
  • Maurice Clerc

Abstract

In this paper, we investigate the use of low-discrepancy sequences to generate an initial population for population-based optimization algorithms. Previous studies have found that low-discrepancy sequences generally improve the performance of a population-based optimization algorithm. However, these studies generally have some major drawbacks like using a small set of biased problems and ignoring the use of non-parametric statistical tests. To address these shortcomings, we have used 19 functions (5 of them quasi-real-world problems), two popular low-discrepancy sequences and two well-known population-based optimization methods. According to our results, there is no evidence that using low-discrepancy sequences improves the performance of population-based search methods. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Mahamed Omran & Salah al-Sharhan & Ayed Salman & Maurice Clerc, 2013. "Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms," Computational Optimization and Applications, Springer, vol. 56(2), pages 457-480, October.
    • Handle: RePEc:spr:coopap:v:56:y:2013:i:2:p:457-480
    DOI: 10.1007/s10589-013-9559-2
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    References listed on IDEAS

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    1. William M. Spears & Derek T. Green & Diana F. Spears, 2010. "Biases in Particle Swarm Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 1(2), pages 34-57, April.
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