Journal Article10.1016/J.ASOC.2015.12.007
A multi-objective evolutionary algorithm based QoS routing in wireless mesh networks
R. Murugeswari,Sudhakar Radhakrishnan,D. Devaraj +2 more
- 01 Mar 2016
- Vol. 40, pp 517-525
40
TL;DR: A new model for routing in WMN is proposed by using Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II), which improves the throughput and minimizes the transmission delay for varying number of nodes and higher mobility scenarios.
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Abstract: Graphical abstractDisplay Omitted HighlightsThe present work proposes a Modified Non-dominated Sorting Genetic Algorithm-II which is applied to the routing problem in WMN.Dynamic crowding distance strategy aims to improve the diversity of the non-dominated solution.Analytic hierarchy process is used to find the best compromise solution.Compared to NSGA-II, the proposed algorithm can improve the throughput and minimizes the transmission delay. 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.
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Citations
A survey on QoS mechanisms in WSN for computational intelligence based routing protocols
Tarunpreet Kaur,Dilip Kumar +1 more
TL;DR: This paper presents a systematic review on the QoS mechanisms that have been employed by routing protocols and also highlights the performance issues of each mechanism and presents a comparative analysis of computational intelligence based QoS-aware routing protocols with their strengths and limitations.
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An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups
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A velocity-based butterfly optimization algorithm for high-dimensional optimization and feature selection
TL;DR: In this article , a modified position updated equation is designed by introducing velocity item and memory item in the local search phase to guide the search for candidate individuals, and a novel refraction-based learning strategy is introduced into butterfly optimization algorithm to effectively enhance diversity and exploration.
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Delay- and Interference-Aware Routing for Wireless Mesh Network
Yuan Chai,Xiao-Jun Zeng +1 more
TL;DR: A delay- and interference-aware routing (DIAR) method using optimization is proposed in this article to find effective routes in a wireless mesh network to obtain better network performance.
33
A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
Zitong Wang,Yan Pei,Jianqiang Li +2 more
TL;DR: A comprehensive survey of MOEA algorithms can be found in this paper , which summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy, i.e., decomposition-based, dominant relation-based and evaluation index-based algorithms.
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
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