کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380461 1437443 2014 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An adaptive memetic Particle Swarm Optimization algorithm for finding large-scale Latin hypercube designs
ترجمه فارسی عنوان
یک الگوریتم بهینه سازی ذرات مکمل سازنده برای پیدا کردن طرح های بزرگ در مقیاس لاتین
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
The Latin Hypercube (LH) design problem arises in computer simulations and is employed for examination and simulation of many physical events. In order to find a (near) optimal LH design, this paper proposes a new version of Particle Swarm Optimization (PSO) algorithm, which uses a population-based optimizer as the evolutionary part and a multiple-local-search procedure as the refinement part of the algorithm. To manage the problem constraints, the proposed algorithm utilizes a Ranked Ordered Value (ROV) rule, which converts the continuous space of solutions to the point-permutation space. Furthermore, to maintain the population diversity, the meta-Lamarckian learning strategy is applied to the local search procedure of the algorithm. In order to test the proposed algorithm, we compare it with a set of existing algorithms on several problem instances. To perform a fair comparison, the best parameters for all the algorithms are found and the experiments are performed based on these parameters. The experimental results show that the proposed algorithm outperforms the existing algorithms in solving large-scale LH design problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 36, November 2014, Pages 222-237
نویسندگان
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