Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

Dhiman, Gaurav; Kaur, Amandeep

doi:10.3390/designs2030028

Cited by 76 publications

(19 citation statements)

References 46 publications

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“…The various applications [11][12][13][14][15] in which IOT can comprise security and surveillance, automation of agriculture, healthcare, traffic management, the emergence of smart cities, etc. [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Zhang

Duan

et al. 2021

Wireless Communications and Mobile Computing

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The technological advent in smart sensing devices and the Internet has provided practical solutions in various sectors of networking, public and private sector industries, and government organizations worldwide. This study intends to combine the Internet of Things (IoT) technology with health monitoring to make it personalized and timely through allowing the interconnection between the devices. This work is aimed at exploring various wearable health monitoring modules that people wear to monitor heart rate, blood pressure, pulse, body temperature, and physiological information. The information is acquired using the wireless sensor to create a health monitoring system. The data is integrated using the Internet of Things for processing, connecting, and computing to achieve real-time monitoring. The temperature of three people measured by the temperature thermometer is 36.4, 36.7, and 36.5 (°C), respectively, and the average acquired by the monitoring system of the three people is 36.5, 36.4, and 36.5 (°C), respectively, indicating that the system demonstrated relatively accurate and stable testability. The user’s ECG is displayed clearly and conveniently using the ECG acquisition system. The pulse rate of the three people tested by the system is 78, 78, and 79 (times/min), respectively, similar to the medical pulse meter results. The physiological information acquired using the semantic recognition, matching system, and character matching system is relatively accurate. It concludes that the human health monitoring system based on the Internet of Things can provide people with daily health management, instrumental in heightening health service quality and level.

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Section: Introductionmentioning

confidence: 99%

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Zhang

Duan

et al. 2021

Wireless Communications and Mobile Computing

Self Cite

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“…Experimental results show that the algorithm has better performance [27], [28]. SHO is proven to be superior to other optimization algorithms in optical buffer design and airfoil design problems [29]. SHO is better than other optimization algorithms in solving economic load power scheduling problem [30].…”

Section: Wrappers Have Three Main Factors: Search Techniques Formentioning

confidence: 95%

Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

Jia

Song

et al. 2019

IEEE Access

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The purpose of this paper is to propose a new hybrid metaheuristic to solve the problem of feature selection. Feature selection problem is the process of finding the most relevant subset based on some criteria. A hybrid metaheuristic is a new trend in the development of optimization algorithms. In this paper, two different hybrid models are designed based on spotted hyena optimization (SHO) for feature selection problem. The SHO algorithm can find the optimal or nearly optimal feature subset in the feature space to minimize the given fitness function. In the first model, the simulated annealing algorithm (SA) is embedded in the SHO algorithm (called SHOSA-1) to enhance the optimal solution found by the SHO algorithm after each iteration. In the second model, SA enhances the final solution obtained by the SHO algorithm (called SHOSA-2). The performance of these methods is evaluated in 20 datasets in the UCI repository. The experiments show that SHOSA-1 performs better than the native algorithm and SHOSA-2. And then, SHOSA-1 is compared with six state-of-the-art optimization algorithms. The experimental results confirm that SHOSA-1 improves the classification accuracy and reduces the number of selected features compared with other wrapper-based optimization algorithms. That proves the excellent performance of SHOSA-1 in spatial search and feature attribute selection.

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“…In 2018, Dhiman and Kumar cast off SHO to resolve two real‐life engineering problems such as optical buffer design problem and air foil design problem. The technique they have used in the method is law of gravitation and mathematically modeled and simulated behavior of spotted hyenas . In 2018, Dhiman and Kumar cast off SHO to solve convex and nonconvex monetary dispatch problem.…”

Section: Background Researchmentioning

confidence: 99%

“…The technique they have used in the method is law of gravitation and mathematically modeled and simulated behavior of spotted hyenas. 37 In 2018, Dhiman and Kumar cast off SHO to solve convex and nonconvex monetary dispatch problem. The newly proposed method has been verified by the help of different generator systems and also compared with other popular approaches to confirm its efficiency.…”

Section: Background Researchmentioning

confidence: 99%

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

Panda

Majhi

2020

Computational Intelligence

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Spotted hyena optimizer (SHO) is a recently developed swarm‐based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)‐SHO. In STS‐SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real‐world application, the said algorithm has been applied to train pi‐sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS‐SHO is an effective and trustworthy algorithm, which has the ability to resolve real‐life optimization complications.

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Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

Cited by 76 publications

References 46 publications

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

[Retracted] A Detailed Research on Human Health Monitoring System Based on Internet of Things

Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

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