Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Shaikh, Muhammad Suhail; Chen, Hua; Jatoi, Munsif Ali; Ansari, Muhammad Mohsin; Qader, Aleem Ahmed

doi:10.1049/smt2.12023

Cited by 39 publications

(13 citation statements)

References 80 publications

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“…The overall complexity for maximum number of iterations is 𝑂 𝑓 × 𝑧 × 𝑀𝑎𝑥𝐼𝑡𝑒𝑟 , where 𝑀𝑎𝑥𝐼𝑡𝑒𝑟 represents the maximum number of iterations. The computation time of GWO is lower than that of many other metaheuristic approaches such as GA, PSO and harmony search algorithm [68]. Fig.…”

Section: Complexity Analysis Of Hlpa B5g-iotmentioning

confidence: 95%

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Kaushik

Al-Raweshidy

2022

IEEE Access

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Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%,

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Section: Complexity Analysis Of Hlpa B5g-iotmentioning

confidence: 95%

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Kaushik

Al-Raweshidy

2022

IEEE Access

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“…The best optimum value is obtained based on the proposed algorithm. 26 This paper proposes a parameter estimation method for the TL parameter considering different bundle conductors using a WOA. To determine single and three-phase TL parameters, the systems need an optimization algorithm with the following characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…Shaikh suggested a gray wolf optimization algorithm for the overhead TL parameter considering various bundle conductors. The best optimum value is obtained based on the proposed algorithm 26 . This paper proposes a parameter estimation method for the TL parameter considering different bundle conductors using a WOA.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter estimation of overhead transmission line considering different bundle conductors with the uncertainty of load modeling

Shaikh

Chen

Hassan

et al. 2021

Optim Control Appl Methods

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The overhead power line is used for the transmission and distribution of electrical power across long distances. It consists of multiple conductors mounted on poles or towers. The parameters of the overhead transmission lines (TLs) are dependent upon certain factors that are associated with bundle conductors. This research work uses a whale optimization algorithm (WOA) to calculate the overhead AC TL parameters. The proposed technique is applied to single‐phase and three‐phase AC TLs. It can be observed that within an AC TL, a change in one parameter will cause the corresponding variation in other parameters. This work has determined capacitance and inductance per unit length for three‐phase using proposed WOA algorithms at different bundle conductor arrangements. However, the study of load modeling for voltage stability is also presented.

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“…Grey wolf optimizer (GWO) is also a meta-heuristic optimizer that imitates the social behaviour of grey wolves such as leadershiphierarchyand the group hunting. GWO has the superior abilities to circumvent algorithms getting stuck into local optimal value [6]. It also has a fast convergence speed in finding the optimum solution.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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Diabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

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Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Cited by 39 publications

References 80 publications

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Optimal parameter estimation of overhead transmission line considering different bundle conductors with the uncertainty of load modeling

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

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