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IDENTIFICATION AND CONTROL OF A HEAT FLOW SYSTEM BASED ON THE TAKAGI-SUGENO FUZZY MODEL USING THE grey wolf OPTIMIZER

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THERMAL SCIENCE 2022年第3期26卷 2275-2286页

作者： Jovanovic, Radisa Z. Zaric, Vladimir R. Univ Belgrade Fac Mech Engn Belgrade Serbia

Even though, it is mostly used by process control engineers, the temperature control remains an important task for researchers. This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found. From these identified models a Takagi-Sugeno model is obtained using fixed membership functions in the premises of the rules. According to the chosen objective function, parameters in the premise part of Takagi-Sugeno fuzzy model were optimized using the grey wolf algorithm. Furthermore, by using the parallel distributed compensation a fuzzy controller is developed via the fuzzy blending of three proportional + sum controllers designed for each of the operating points. In order to evaluate performance, a comparison is made between the fuzzy controller and local linear controllers. Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared. The experimental results on a heat flow platform are presented to validate efficiency of the proposed method.

关键词： temperature control grey wolf optimization algorithm parallel distributed compensation fuzzy control Takagi-Sugeno discrete-time systems

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An Enhanced grey wolf Optimizer for Numerical optimization

An Enhanced Grey Wolf Optimizer for Numerical Optimization

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Sharma, Sakshi Salgotra, Rohit Singh, Urvinder Thapar Univ Dept Elect & Commun Patiala Punjab India

ISBN: (纸本)9781509032945

grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.

关键词： grey wolf optimization algorithm bat algorithm flower pollination algorithm numerical optimization benchmark problems

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Cloud infrastructure availability optimization using Dragonfly and greywolf optimization algorithms for health systems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6209-6227页

作者： Saini, Monika Maan, Vijay Singh Kumar, Ashish Saini, Dinesh Kumar Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 Rajasthan India Manipal Univ Jaipur Dept Comp & Commun Engn Jaipur Rajasthan India

Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 byGWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time.

关键词： Availability cloud infrastructure dragonfly algorithm grey wolf optimization algorithm markov process

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Weak bus determination and Real power loss minimization using grey wolf optimization 6

Weak bus determination and Real power loss minimization usin...

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6th IEEE International Conference on Power Systems (ICPS)

作者： Raj, Saurav Bhattacharyya, Biplab Indian Sch Mines Dept Elect Engn Dhanbad Bihar India

ISBN: (纸本)9781509001286

A new optimization algorithm approach is proposed named grey wolf optimization algorithm for the solution of reactive power planning (RPP) of a connected power network based on the detection of weak buses determined by line stability index method and Fast voltage stability index. The optimization algorithm is then applied on standard IEEE 30 and IEEE 57 bus system. The objective of the proposed work is to minimize active power loss and operating cost by proper coordination of reactive power generation of the generators, transformer tap setting positions and shunt capacitors placed at the weak buses of the system while maintaing voltage profile within acceptable limit. Finally, it is observed that reactive power planning with line stability index method yields better result than planning with fast voltage stability index.

关键词： active power loss operating cost voltage collapse prediction fast voltage stability index grey wolf optimization algorithm reactive power planning

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Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第13期32卷 e4032-e4032页

作者： Karthikeyan, Anbalagan Srividhya, Venkatasubramanian Kundu, Souvik Vellore Inst Technol Sch Elect & Commun Engn Vellore Tamil Nadu India Vellore Inst Technol Vellore 632014 Tamil Nadu India

Effective use of unused licensed spectrum by secondary nodes without causing any harmful interference to the primary users is one of the challenging task, and in this paper, a detailed description of joint spectrum sensing and resource allocation in cognitive radio (CR) networks is discussed. The CR system utility is maximized by joint spectrum sensing and channel resource allocation scheme based on random walk grey wolf optimization (RW-GWO) algorithm for frequency hopping cognitive radio-based networks. In this paper, the nodes will sense the presence of primary users in the same channel and move to another channel in the guidance of a guide node (GN) if primary signal is detected in the channel. In order to achieve collision-free communication, primary signals and secondary signals use the channels strategically and the mechanism is described in this paper. RW-GWO is used to derive the optimum sensing and data transmission schedules. It selects the sensing nodes to sense the spectrum and other nodes take part in transmitting the data. GNs not only guide the nodes to hop to the next channel if the primary channel is detected, but also distribute the nodes in different available secondary hopping channels. Simulation results show that reliable spectrum sensing and efficient channel allocation can be achieved in our proposed algorithm.

关键词： channel allocation cognitive radio grey wolf optimization algorithm optimization random walk

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Robust multi-user detection based on hybrid grey wolf optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第15期33卷 e5273-e5273页

作者： Sun, Xiyan Fan, Zhuo Ji, Yuanfa Wang, Shouhua Yan, Suqing Wu, Sunyong Fu, Qiang Ghazali, Kamarul Hawari Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Malaysia

The search for an effective nature-inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi-user detection algorithm based on grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi-user detection under an impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.

关键词： differential evolution algorithm grey wolf optimization algorithm hybrid optimization multi-user detection impulse noise

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Research on the motion trajectory control of industrial manipulators by the incremental PID control optimization algorithm

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MECHANICS & INDUSTRY 2025年 26卷

作者： Wang, Jicai Chen, Fangyan Jiaxing Nanyang Polytech Inst Jiaxing 314031 Zhejiang Peoples R China

With the wide application of industrial manipulators, the research of its moving trajectory control method has become more and more important content. In this paper, the motor closed-loop control strategy of an industrial 6R manipulator was briefly introduced, the proportional, integral, and differential ( (PID) algorithm commonly used in motor control was analyzed, and its shortcomings were pointed out. Then, the incremental PID algorithm was introduced to design a grey wolf optimization (IGWO) algorithm to achieve parameter tuning. Simulation experiments were carried out in a MATLAB environment. The results showed that the incremental PID algorithm generated a smaller control deviation than the PID algorithm. The average deviation of the three axes of the incremental PID algorithm undergoing IGWO parameter tuning was less than 0.2 mm, and the moving time was between 1.1 s and 1.3 s. The control accuracy and efficiency of the incremental PID algorithm undergoing IGWO parameter tuning were better than those of the incremental PID algorithm whose parameters were tuned using the trial-and-error method and the grey wolf optimization algorithm respectively. Moreover, better stability and anti-interference were observed in step response and sinusoidal response. The results prove that the incremental PID algorithm combined with the IGWO algorithm is reliable in the moving trajectory control of industrial manipulators and can be applied in practice.

关键词： Incremental PID control industrial manipulator grey wolf optimization algorithm sine response

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Robust design and best control channel selection of FACTs-based WADC for improving power system stability using grey wolf Optimizer

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ENERGY REPORTS 2024年 11卷 55-70页

作者： Davoudkhani, Iraj Faraji Shakarami, Mahmoud Rerza Abdelaziz, Almoataz Y. El-Shahat, Adel Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran Lorestan Univ Dept Elect Engn Lorestan Iran Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Purdue Univ Sch Engn Technol W Lafayette IN 47907 USA

With recent advances in wide-area measurement systems (WAMS), wide-area damping controllers (WADCs) based on remote signals can effectively damp inter-area oscillations. However, these signals are inherently subjected to communication time-delay which may adversely affect the damping of inter area oscillations. The purpose of this paper is to present a optimization-based method for designing a WADC for a static synchronous series compensator (SSSC) to improve the damping of inter-area oscillations by considering the time delays of remote control signals. For this aim, the control gain value of the WADC, shifting of the critical modes to a desirable area, and the maximum time delay margins are simultaneously considered as an objective function. Moreover, to design a WADC as minimum-phase structure, the suitable constraints have been determined and added to the objective function as penalty factors. A grey wolf optimization (GWO) algorithm is utilized to solve the optimization problem. Robustness of the designed SSSC-based WADC in amplitude and phase control channels against the time delay has been compared and then the best control channels to damp inter-area oscillations has been selected. Based on simulations and statistical results conducted on 4 and 50 machine multimachine power systems, it is evident that the proposed method based on the GWO outperforms other techniques in achieving the optimal design of a SSSC-based WADC for damping inter-area oscillations.

关键词： SSSC Wide area damper Time delay Inter -area oscillation grey wolf optimization algorithm Control channel

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A hybrid cluster-borderline SMOTE method for imbalanced data of rock groutability classification

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第1期81卷 1-15页

作者： Li, Kai Ren, Bingyu Guan, Tao Wang, Jiajun Yu, Jia Wang, Kexiang Huang, Jicun Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety 92 Weijin Rd Tianjin 300072 Peoples R China China Three Gorges Construct Engn Corp Yichang Peoples R China

Groutability classification is highly important for guaranteeing the safety and quality of grouting projects. However, the precision of groutability classification is inevitably influenced by imbalanced data, in which most fractured rock masses are groutable. Current studies cannot realize high-precision classification for minority classes without considering imbalanced data. Although synthetic minority oversampling technique (SMOTE) is the most influential oversampling method, it produces redundant samples and noise labels. To address these issues, a hybrid cluster-borderline SMOTE method (HCBS) is proposed in this paper. The weights of samples near the minority class center and border were improved to present the category feature, thereby solving the redundant samples problem of SMOTE. To restrain noise, the total samples were divided into different clusters by k-means, and a cluster with an imbalance ratio of more than one was selected to generate new samples. The negative majority samples near the minority class border were removed by variant borderline SMOTE to clean the dataset from noisy instances, and the original and produced minority samples were reduced to a certain ratio according to the majority samples. Finally, the classification precision of the proposed HCBS method was verified using random forest (RF) in grouting engineering, and the number of trees and split variables of the tree nodes were optimized using the grey wolf optimization (GWO) algorithm. The proposed HCBS-GRF method outperformed RF, GWO-optimized RF (GRF), SMOTE-GRF, density SMOTE-GRF, borderline density SMOTE-GRF, and other competitive methods, thereby providing the highest groutability classification accuracy with the least number of generated instances.

关键词： Groutability classification Imbalanced data SMOTE Oversampling k-means clustering Random forest grey wolf optimization algorithm

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A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2021年第2期66卷 181-200页

作者： Cetinkaya, Mehmet Bahadir Duran, Hakan Univ Erciyes Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.

关键词： artificial bee colony algorithm differential evolution algorithm firefly algorithm grey wolf optimization algorithm harmony search algorithm particle swarm optimization algorithm retinal vessel segmentation teaching learning based optimization algorithm

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