Applying hybrid genetic–PSO technique for tuning an adaptive PID controller used in a chemical process

El-Gendy, Eman M.; Saafan, Mahmoud M.; Elksas, Mohamed S.; Saraya, Sabry F.; Areed, Fayez F.

doi:10.1007/s00500-019-04106-z

Cited by 28 publications

(11 citation statements)

References 44 publications

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“…The execution procedure of PSO is simple as well as easy to implement due to lessor requirements of the memory [26] . Recently, PSO is applied in nonlinear electric circuits [27] , pitch control system of wind turbine [28] , parameter approximation [29] , reactive power dispatch generation [30] , benchmark optimization models [31] , tune an adaptive PID controller [32] and approximation of undrained shear soil strength [33] .…”

Section: Methodsmentioning

confidence: 99%

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

Umar

Sabir

Raja

et al. 2021

Alexandria Engineering Journal

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Section: Methodsmentioning

confidence: 99%

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

Umar

Sabir

Raja

et al. 2021

Alexandria Engineering Journal

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“…For example, Chu et al (2020) applied a nondominated sorting genetic algorithm-II (NSGA-II), as a multiobjective optimization method derived from GA, to optimize a fuzzy proportional-integral-derivative (PID) controller for automatic train operation. El-Gendy et al (2020) proposed a hybrid of GA and PSO to tune the parameters of different adaptive PID controllers.…”

Section: The Optimization Of Fuzzy Inference Systemsmentioning

confidence: 99%

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Čubranić-Dobrodolac

Švadlenka

Čičević

et al. 2021

Preprint

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This paper examines how a driver’s perception of various speed levels, as well as driver’s speed perception from different positions, affect the propensity for motor vehicle crashes (MVCs). Data collection is performed in twelve experiments. 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. To process data, seven Type-2 fuzzy inference systems (T2FISs) are designed and tested in a sense of compliance with the empirical data. As a result, a relationship between the various forms of speed perception and participation in MVCs can be quantified. To examine the initial conclusions, the optimization of each of these T2FISs is performed by implementing the bee colony optimization (BCO) metaheuristic. The BCO based algorithm proposed in this paper achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. The best-found T2FIS structures can be used as a decision-making tool that quantifies the driver propensity for MVCs, which can be useful in various educational and recruitment procedures in the field of transportation and traffic safety.

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“…Examples of these algorithms are Genetic Algorithms (GA) ( Holland, 1992 ), Particle Swarm Optimization (PSO) ( Kennedy & Eberhart, 1995 ), Cuckoo Search (CS) algorithm ( Yang & Deb, 2010 ), Grasshopper Optimization Algorithm (GOA) ( Balaha and Saafan, 2021 , Saremi et al, 2017 ), and Grey Wolf Optimizer (GWO) ( Mirjalili et al, 2014 ). Also, many learning techniques have been used to improve the performance of the metaheuristic algorithms ( El-Gendy et al, 2020 , Feng et al, 2018 , Li, Li, Tian, and Xia, 2019 , Li, Li, Tian, and Zou, 2019 , Li and Wang, 2021 , Li, Wang, and Alavi, 2020 , Li, Wang, Dong, et al, 2021 , Li, Wang, and Gandomi, 2021 , Li, Wang, and Wang, 2021 , Li, Xiao, et al, 2020 , Nan et al, 2017 , Saafan and El-Gendy, 2021 , Wang, Deb, et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

Balaha

El-Gendy

Saafan

2021

Expert Systems with Applications

Self Cite

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Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19) has propagated fast all over the world, affecting the lives of billions of people and increasing the mortality rate worldwide in few months. The golden treatment against the invasive spread of COVID-19 is done by identifying and isolating the infected patients, and as a result, fast diagnosis of COVID-19 is a critical issue. The common laboratory test for confirming the infection of COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, these tests suffer from some problems in time, accuracy, and availability. Chest images have proven to be a powerful tool in the early detection of COVID-19. In the current study, a hybrid learning and optimization approach named CovH2SD is proposed for the COVID-19 detection from the Chest Computed Tomography (CT) images. CovH2SD uses deep learning and pre-trained models to extract the features from the CT images and learn from them. It uses Harris Hawks Optimization (HHO) algorithm to optimize the hyperparameters. Transfer learning is applied using nine pre-trained convolutional neural networks (i.e. ResNet50, ResNet101, VGG16, VGG19, Xception, MobileNetV1, MobileNetV2, DenseNet121, and DenseNet169). Fast Classification Stage (FCS) and Compact Stacking Stage (CSS) are suggested to stack the best models into a single one. Nine experiments are applied and results are reported based on the Loss, Accuracy, Precision, Recall, F1-Score, and Area Under Curve (AUC) performance metrics. The comparison between combinations is applied using the Weighted Sum Method (WSM). Six experiments report a WSM value above 96.5%. The top WSM and accuracy reported values are 99.31% and 99.33% respectively which are higher than the eleven compared state-of-the-art studies

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Applying hybrid genetic–PSO technique for tuning an adaptive PID controller used in a chemical process

Cited by 28 publications

References 44 publications

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

Integrated neuro-swarm heuristic with interior-point for nonlinear SITR model for dynamics of novel COVID-19

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning

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