دانلود مقاله ISI انگلیسی شماره 78827
ترجمه فارسی عنوان مقاله

مسیریابی QoS مبتنی بر الگوریتم تکاملی چند هدفه در شبکه های مش بی سیم

عنوان انگلیسی
A multi-objective evolutionary algorithm based QoS routing in wireless mesh networks
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
78827 2016 9 صفحه PDF
منبع

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

Journal : Applied Soft Computing, Volume 40, March 2016, Pages 517–525

ترجمه کلمات کلیدی
فاصله ازدحام پویا - بهینه سازی چندمنظوره؛ نیمه متقاطع نقشه برداری - فرآیند تحلیل سلسله مراتبی
کلمات کلیدی انگلیسی
Dynamic crowding distance; Multiobjective optimization; Partial mapped crossover; Analytic hierarchy process
پیش نمایش مقاله
پیش نمایش مقاله  مسیریابی QoS مبتنی بر الگوریتم تکاملی چند هدفه در شبکه های مش بی سیم

چکیده انگلیسی

The huge demand for real time services in wireless mesh networks (WMN) creates many challenging issues for providing quality of service (QoS). Designing of QoS routing protocols, which optimize the multiple objectives is computationally intractable. This paper proposes a new model for routing in WMN by using Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II). The objectives which are considered here are the minimization of expected transmission count and the transmission delay. In order to retain the diversity in the non-dominated solutions, dynamic crowding distance (DCD) procedure is implemented in NSGA-II. The simulation is carried out in Network Simulator 2 (NS-2) and comparison is made using the metrics, expected transmission count and transmission delay by varying node mobility and by increasing number of nodes. It is observed that MNSGA-II improves the throughput and minimizes the transmission delay for varying number of nodes and higher mobility scenarios. The simulation clearly shows that MNSGA-II algorithm is certainly more suitable for solving multiobjective routing problem. A decision-making procedure based on analytic hierarchy process (AHP) has been adopted to find the best compromise solution from the set of Pareto-solutions obtained through MNSGA-II. The performance of MNSGA-II is compared with reference point based NSGA-II (R-NSGA-II) in terms of spread.