دانلود مقاله ISI انگلیسی شماره 78881
عنوان فارسی مقاله

چارچوب کلی برای الگوریتم های تکاملی چند هدفه متمرکز شده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78881 2014 25 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
General framework for localised multi-objective evolutionary algorithms
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 258, 10 February 2014, Pages 29–53

کلمات کلیدی
بهینه سازی چند هدفه؛ الگوریتم تکاملی؛ چارچوب؛ خوشه بندی
پیش نمایش مقاله
پیش نمایش مقاله چارچوب کلی برای الگوریتم های تکاملی چند هدفه متمرکز شده

چکیده انگلیسی

Many real-world problems have multiple competing objectives and can often be formulated as multi-objective optimisation problems. Multi-objective evolutionary algorithms (MOEAs) have proven very effective in obtaining a set of trade-off solutions for such problems. This research seeks to improve both the accuracy and the diversity of these solutions through the local application of evolutionary operators to selected sub-populations. A local operation-based implementation framework is presented in which a population is partitioned, using hierarchical clustering, into a pre-defined number of sub-populations. Environment-selection and genetic-variation are then applied to each sub-population. The effectiveness of this approach is demonstrated on 2- and 4-objective benchmark problems. The performance of each of four best-in-class MOEAs is compared with their modified local operation-based versions derived from this framework. In each case the introduction of the local operation-based approach improves performance. Further, it is shown that the combined use of local environment-selection and local genetic-variation is better than the application of either local environment-selection or local genetic-variation alone. Preliminary results indicate that the selection of a suitable number of sub-populations is related to problem dimension as well as to population size.

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