کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
381627 | 1437512 | 2006 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multi-objective genetic algorithms: A way to improve the convergence rate
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
Multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process; moreover, the solutions belonging to the frontier are equally distributed along the frontier. This improvement is due to an extension function based on a natural phenomenon, which is similar to a cyclical epidemic which happens every N generations (eN-MOGA). The use of this function enables a faster convergence of the algorithm by reducing the necessary number of generations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 19, Issue 5, August 2006, Pages 501–510
Journal: Engineering Applications of Artificial Intelligence - Volume 19, Issue 5, August 2006, Pages 501–510
نویسندگان
O.B. Augusto, S. Rabeau, Ph. Dépincé, F. Bennis,