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

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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.

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments that helped to improve the paper.

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Correspondence to Mahamed G. H. Omran.

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Appendix: Source code listing in Matlab (PDF 43 kB)

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Omran, M.G.H., al-Sharhan, S., Salman, A. et al. Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms. Comput Optim Appl 56, 457–480 (2013). https://doi.org/10.1007/s10589-013-9559-2

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