Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms |
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Authors: | Mahamed G. H. Omran Salah al-Sharhan Ayed Salman Maurice Clerc |
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Affiliation: | 1. Department of Computer Science, Gulf University for Science and Technology, Kuwait City, Kuwait 2. Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait 3. Independent Consultant, Groisy, France
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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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