Discrete multi-objective differential evolution algorithm for routing in wireless mesh network

Murugeswari, R.; Radhakrishnan, S.

doi:10.1007/s00500-015-1730-5

Cited by 6 publications

(5 citation statements)

References 38 publications

(31 reference statements)

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“…However, unlike traditional EAs, the DE algorithm is much simpler to implement, with only a few parameters to be set. Therefore, DE has drawn much attention and has been successfully applied in numerous domains of science and engineering (see e.g., [197], [198]).…”

Section: B Moo Algorithmsmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

371

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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Section: B Moo Algorithmsmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

371

202

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show abstract

“…Considering different requests of Quality of Service (QoS), different aspects like Expected Transmission Time (ETT) [11] , Expected Transmission Count (ETX) [12] , throughput [13] , packet error ratio [14] , energy consumption [15] , hop count [16] and so on can be considered. When solving the established routing optimization problem, genetic algorithm (GA) [11] , particle swarm optimization [17] , multi-objective differential evolution (MODE) [18] , ant colony optimization (ACO) [19] , reinforcement learning [20,21] , fuzzy logic [22,23] , etc. can be used.…”

Section: Routing Designmentioning

confidence: 99%

The development of green wireless mesh network: A survey

Chai¹,

Zeng²

2021

JSEGC

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Wireless mesh network (WMN) is a type of self-healing, self-configuration, and peer-to-peer wireless network. Without expensive and fixed base stations, WMN can be established fast, easily, and flexibly at a low cost. The convenience and flexibility of establishing WMN brings a wide range of applications. The request of high bandwidth, high coverage and high transmission rate can be satisfied. Current research on WMN involves routing, resource allocation, mobility control, security and so on. With the increasing concern of green communication, improving energy efficiency is more and more important nowadays. As energy harvesting can overcome energy constraints and extend the network lifetime, it has attracted the great attention from many researchers when designing network. Further, WMN is an important networking mode in next-generation communication to guarantee the quality of service and reduce the networking complexity. Due to such importance, a survey of the research and development in WMN including the future research direction and opportunities is given in this paper.

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“…Multicast routing is often preferred strategy in quality service delivery, especially in multichannel multiradio wireless mesh networks. DE has shown to be efficient in finding the optimal performance in routing [141][142], packet delivery ratio maximisation [143], delay minimisation [144] and optimum reassigning vacant channel to cognitive users without network deterioration [145]. Assignment can also take the form of allocation in download link systems [146].…”

Section: Quality Of Service Improvementmentioning

confidence: 99%

Differential Evolution in Wireless Communications: A Review

Okagbue¹,

Adamu

Anake

2019

Int. J. Onl. Eng.

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<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p>

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Discrete multi-objective differential evolution algorithm for routing in wireless mesh network

Cited by 6 publications

References 38 publications

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

The development of green wireless mesh network: A survey

Differential Evolution in Wireless Communications: A Review

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