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

  • Authors:

  • Mahamed G. Omran;Salah Al-Sharhan;Ayed Salman;Maurice Clerc

  • Affiliations:

  • Department of Computer Science, Gulf University for Science and Technology, Kuwait City, Kuwait;Department of Computer Science, Gulf University for Science and Technology, Kuwait City, Kuwait;Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait;Independent Consultant, Groisy, France

  • Venue:
  • Computational Optimization and Applications
  • Year:
  • 2013

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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.