دانلود مقاله ISI انگلیسی شماره 78788
عنوان فارسی مقاله

MOEA/D-ARA+SBX: یک الگوریتم تکاملی چند هدفه جدید بر اساس تجزیه با الگوریتم قطرات باران مصنوعی و متقاطع باینری شبیه سازی شده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78788 2016 22 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover
منبع

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

Journal : Knowledge-Based Systems, Volume 107, 1 September 2016, Pages 197–218

کلمات کلیدی
بهینه سازی چند هدفه؛ الگوریتم قطرات باران مصنوعی؛ تجزیه؛ همسایه های K- نزدیکترین؛ آرشیو نخبه گرا خارجی؛ تجزیه و تحلیل همگرایی
پیش نمایش مقاله
پیش نمایش مقاله MOEA/D-ARA+SBX: یک الگوریتم تکاملی چند هدفه جدید بر اساس تجزیه با الگوریتم قطرات باران مصنوعی و متقاطع باینری شبیه سازی شده

چکیده انگلیسی

In the field of optimization computation, there has been a growing interest in applying intelligent algorithms to solve multi-objective optimization problems (MOPs). This paper focuses mainly on the multi-objective evolutionary algorithm based on decomposition, MOEA/D for short, which offers a practical general algorithmic framework of evolutionary multi-objective optimization, and has been achieved great success for a wide range of MOPs. Like most other algorithms, however, MOEA/D has its limitations, which are reflected in three aspects: the problem of balancing diversity and convergence, non-uniform distribution of the Pareto front (PF), and weak convergence of the algorithm. To alleviate these limitations, a new combination of the artificial raindrop algorithm (ARA) and a simulated binary crossover (SBX) operator is first integrated into the framework of MOEA/D to balance the convergence and diversity. Thus, our proposed approach is called MOEA/D with ARA and SBX (MOEA/D-ARA+SBX). On the other hand, the raindrop pool in ARA is further extended to an external elitist archive, which retains only non-dominated solutions and discards all others. In addition, the k-nearest neighbors approach is introduced to prune away redundant non-dominated solutions. In such a way, a Pareto approximate subset with good distribution to the true PF may be achieved. Based on the relevant mathematical theory and some assumptions, it is proven that MOEA/D-ARA+SBX can converge to the true PF with probability one. For performance evaluation and comparison purposes, the proposed approach was applied to 44 multi-objective test problems with all types of Pareto set shape, and compared with 16 other versions of MOEA/D. The experimental results indicate its advantages over other approaches.

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