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

Mahamed Omran (), Salah al-Sharhan (), Ayed Salman () and Maurice Clerc ()

Computational Optimization and Applications, 2013, vol. 56, issue 2, 457-480

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

Keywords: Low-discrepancy sequences; Quasi-random sequences; Pseudo-random sequences; Population-based optimization algorithms; Particle swarm optimization; Differential evolution (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s10589-013-9559-2

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