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Optimized intuitionistic fuzzy enriched honey badger algorithm for cloud network-based work load scheduling

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第3期24卷 1851-1862页

作者： Sundaresan, Yuvaraj Gandhi Thiyagarajan, Revathi PSG Coll Arts & Sci Coimbatore Tamil Nadu India

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum honey badger algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters.

关键词： Work load scheduling Intuitionistic fuzzy quantum theory honey badger algorithm resource allocation heterogenous work cloud network

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A novel honey badger algorithm based load frequency controller design of a two-area system with renewable energy sources

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ENERGY REPORTS 2023年 9卷 272-279页

作者： Ozumcan, Sercan Ozturk, Ali Varan, Metin Andic, Cenk Duzce Univ Dept Elect Elect Engn TR-81060 Duzce Turkiye Sakarya Univ Appl Sci Dept Elect Elect Engn TR-54187 Sakarya Turkiye Istanbul Tech Univ Dept Elect Engn TR-34467 Istanbul Turkiye

When it comes to the process of ensuring the stability, quality, and reliability of a power system, one of the most crucial components is known as the load frequency controller (LFC). It does this by ensuring that there is a balance between the amount of power that is produced and the amount that is consumed. This paper proposes a novel evolutionary approach, referred to as the honey badger algorithm (HBA), PI/PID controllers should be configured in the best possible way in order to address the LFC problem in the electrical power system. The research takes into account a power system that is integrated between two areas and uses renewable energy sources, such as a wind system and a solar system. The utilization of renewable energy sources has the potential to yield favorable outcomes in frequency control through the provision of prompt and adaptable responses to fluctuations in system frequency, helping to maintain grid stability. The proposed HBA method is utilized to refine the controller parameter values, using a fitness function anchored on the integral of absolute error (IAE) and the integral time multiplied by absolute error (ITAE). The performance of the proposed HBA-based controller has evaluated under 5% step load perturbation (SLP) in area-1. The HBA-based controllers demonstrate greater performance in terms of settling time, overshoot, and fitness value when compared to other well-known optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization algorithm (WOA), and Grey Wolf Optimization (GWO). According to the obtained results, the IAE-based PID controller has the best performance. The HBA-based PID controller is evaluated according to the following performance criteria;the objective function value is 0.4201, the settling time values and overshoot values for the area-1, area-2 and tieline are 15.6, 33.7 and 27.9 s and -6.6, -0.7 and -0.0071 Hz, respectively. According to the findings, the HBA is both a dependable and effective

关键词： Load frequency controller honey badger algorithm Renewable energy sources

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Optimal wind power generation system by honey badger algorithm with differential evolution strategies

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APPLIED SOFT COMPUTING 2023年第PartA期149卷

作者： Sheng, Wanxing Li, Rui Hui, Hui Lin, Kuo-Ping Lan, Kung-Ming Ren, Qiying Li, Lingling China Elect Power Res Inst Beijing 100192 Peoples R China Tunghai Univ Dept Ind Engn & Enterprise Informat Taichung 40704 Taiwan Hebei Univ Technol Sch Elect Engn Tianjin 300401 Peoples R China

The purpose of this study is to build an optimal hybrid wind power system consisting of a permanent magnet direct-drive wind power generation unit, a hybrid energy storage system (HESS), a power electronic converter, and loads. Moreover, a reasonable control method is designed for each part, and a honey badger algorithm (HBA) with differential evolution strategies is employed to realize the coordinated control of each unit and improve the system stability. The real anti-interference capability of wind power system can be improved by the proposed HBA with differential evolution strategies (IHBA). Firstly, the wind hybrid system model is constructed, in which the wind power generation unit adopts the variable step climbing method to achieve the maximum power point tracking control;the HESS designs the power distribution method based on the bus voltage;the converter supplying energy to the AC load introduces the virtual synchronous generator (VSG) control strategy. Secondly, due to the existence of energy exchange in each unit of the system, VSG has more parameters and its control performance is influenced by the system itself, this study proposes HBA with differential evolution strategies for system parameter optimization and constructs an IHBA-VSG model with the objective of minimizing the bus voltage fluctuation value. The IHBA improves original honey badger algorithm by three mechanisms which are time control function, Gaussian variation factor and differential evolution strategy. Finally, the parameters obtained from the HBA with differential evolution strategies optimization search are substituted into the wind hybrid system, and the simulation system is built by MATLAB/Simulink. The simulation results show that when the environment of the system changes or the load on the AC side changes abruptly, the proposed control method can reduce the bus voltage fluctuation by 2.9%-18.22% compared with the honey badger algorithm Optimized VSG (HBA-VSG), and can reduce the

关键词： New energy Permanent magnet direct-drive wind power generation Hybrid energy storage system Virtual synchronous generator honey badger algorithm

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Optimum control of under-grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第5期44卷 2938-2959页

作者： Brijet, Z. Suresh, T. Velammal Engn Coll Dept Elect & Instrumentat Engn Chennai Tamil Nadu India RMK Engn Coll Dept Elect & Commun Engn Thiruvallur Tamil Nadu India

The minimum air pressure necessary at the grate to penetrate a bed of heated clinker of a certain thickness and permeability is known as under-grate pressure. It is vital to keep the under-grate pressure consistent because excessive pressure causes the clinker to form an unstable suspension and minimum pressure allows heat from the clinker to harm the cooler grates. So it is necessary to obtain excellent performance through reasonable control methods. For this purpose, this article proposes an optimization approach for a proportional integral derivative (PID) controller to control the pressure below the grate of the clinker cooler. The proposed optimization approach is the honey badger algorithm (HBA), which mimics the feeding habits of honey badgers. The main aim of proposed approach is to maintain and regulate under-grate pressure of clinker cooler using HBA and provide optimum cooling rate to clinker. The proposed approach based grate cooler also provide the less electricity consumption and stable temperature. The proposed technique is implemented in MATLAB and its efficiency is compared to various existing techniques. From the simulation, the electricity consumption of proposed approach RMSE is 158 and mean accuracy is 98.65. Under-grate pressure based RMSE is 5.01 and the mean accuracy become 99.55. The outlet temperature based RMSE of proposed approach is 4.32 and mean accuracy is 81.21. These values are lower than existing approaches, it reveals the proposed approach is better than the existing one.

关键词： clinker cooler gain parameters honey badger algorithm pressure of cooler proportional-integral-differential (PID) controller

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An Improved honey badger algorithm through Fusing Multi-Strategies

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Computers, Materials & Continua 2023年第8期76卷 1479-1495页

作者： Zhiwei Ye Tao Zhao Chun Liu Daode Zhang Wanfang Bai School of Computer Science Hubei University of TechnologyWuhan430068China Xining Big Data Service Administration Xining810000China

Thehoney badger algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey *** dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems ***,HBA tends to suffer from the local optimum and low *** alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the *** introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and *** is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering *** Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after *** results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence *** source code is currently available from:https://***/zhaotao789/IHBA.

关键词： honey badger algorithm multi-strategies fusion tent chaotic mapping compound random factors

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An efficient honey badger algorithm for scheduling the microgrid energy management

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ENERGY REPORTS 2023年 9卷 2058-2074页

作者： Fathy, Ahmed Rezk, Hegazy Ferahtia, Seydali Ghoniem, Rania M. Alkanhel, Reem Jouf Univ Fac Engn Elect Engn Dept Sakakah Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig Egypt Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Univ Msila Dept Elect Engn Lab Genie Elect Msila Algeria Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh Saudi Arabia

Nowadays, it is essential for modern grids to operate at optimal scheduling, this helps in reducing the energy costs, mitigating the pollutant emissions, and making better use of renewable energy resources (RESs) such as photovoltaic (PV) and wind turbine (WT). Therefore, this paper proposes an energy management scheme for microgrid (MG) using recent metaheuristic honey badger algorithm (HBA) to enhance its operation via identifying the optimal scheduling of the installed generation units. HBA has the ability to solve complex optimization problem and avoid stuck in local optima due to balance between the exploration and exploitation phases. The constructed MG composes PV, WT, microturbine (MT), fuel cell (FC), and battery storage system. Operation of PV and WT at their normal generations, operation of WT at its rated power, and operation of PV and WT at their maximum limits are three cases analyzed in this work. Two objective functions are considered which are mitigating the operating cost and minimizing the pollutant emission. The proposed HBA is evaluated via conducting comparison to some reported approaches like Fuzzy self-adaptive particle swarm optimizer (FSAPSO), sparrow search algorithm (SSA), and gravitational search and pattern search algorithm (GSA-PS). Moreover, other programmed approaches of aquila optimizer (AO), Tasmanian devil optimizer (TDO), artificial rabbits optimizer (ARO), coronavirus herd immunity optimizer (CHIO), manta-ray foraging optimizer (MRFO), and dynamic arithmetic optimization approach (DAOA) are implemented and compared to the proposed algorithm. The fetched results proved the robustness and preference of HBA in achieving the best operation of MG in all studied operating conditions. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： honey badger algorithm Microgrid energy management Optimal scheduling Renewable energy

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Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with honey badger algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartB期86卷

作者： Narmatha, P. Gupta, Shivani Lakshmi, T. R. Vijaya Manikavelan, D. Sri Sairam Coll Engn Dept Elect & Commun Engn Bengaluru Karnataka India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Mahatma Gandhi Inst Technol Dept Elect & Commun Engn Hyderabad India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India

Melanoma skin cancer poses significant challenges in terms of early detection and accurate diagnosis. It often goes unnoticed in its early stages, leading to advanced diagnoses with poorer treatment outcomes. Diagnosis is subjective and can vary among healthcare professionals, while limited accessibility to dermatologists further delays diagnosis. Addressing these challenges requires innovative solutions, leveraging technology and artificial intelligence, to improve early detection, enhance accuracy, and ultimately improve patient outcomes in melanoma skin cancer. In this paper, Skin cancer identification from dermoscopic images utilizing Deep Siamese domain adaptation convolutional Neural Network optimized with honey badger algorithm is proposed. The proposed method initially performs input image pre-processing to remove label noise and lighting problems. The segmentation was then given the output of the pre-processing. To separate the ROI region, hesitant fuzzy linguistic bi-objective clustering is employed. The improved non-subsampled Shearlet transforms (INSST) region to extract features using the segmented ROI region. Skin cancer and healthy skin are distinguished using the Deep Siamese domain adaptation convolutional Neural Network (DSDACNN). The honey badger algorithm is used to optimize the weight parameters of the DSDACNN. The proposed SKD-DSDACNN-HBA method is carry out in Python. The performance of the proposed SKD-DSDACNN-HBA method attains 15.64%, 20.07% and 25.5% higher F1-score, 24.35%, 29.33% and 35.29% higher Computational time, 24.72%, 29.32% and 36.66% higher AUC and 33.55%, 28.52% and 19.85% lower Error rate while compared with existing methods, like fuzzy k means clustering (SKD-FKMC), handcrafted and non-handcrafted features (SKD-HNHF) and deep learning features with improved moth flame optimization (SKD-DLF-IMFO).

关键词： Melanoma skin detection system Deep Siamese domain adaptation convolutional Neural Network honey badger algorithm ROI region Fuzzy k-means clustering

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honey badger algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年

作者： Prajapati, Shruti Garg, Rachana Mahajan, Priya Delhi Technol Univ Dept Elect Engn Delhi India Delhi Technol Univ Dept Elect Engn Delhi 110042 India

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

关键词： solar photovoltaic honey badger algorithm maximum power point tracking synchronous reference frame theory

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honey badger algorithm Based Tuning of PID Conroller for Load Frequency Control in Two-Area Power System Including Electric Vehicle Battery 4

Honey Badger Algorithm Based Tuning of PID Conroller for Loa...

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4th IEEE Global Power, Energy and Communication Conference (IEEE GPECOM)

作者： Andic, Cenk Ozumcan, Sercan Ozturk, Ali Turkay, Belgin Istanbul Tech Univ ITU Dept Elect Engn Istanbul Turkey Duzce Univ Dept Elect Elect Engn Duzce Turkey

ISBN: (纸本)9781665469258

This paper presents a load frequency control using meta-heuristic method to find the gains of PID controllers. In this study, a novel meta-heuristic method, honey badger algorithm is proposed to find the optimal Kp, Ki and Kd parameters of PID controller. The proposed algorithm is tested on two-area power system. However, electric vehicle battery, which is one of the electrical energy storage devices, has been added to the two-area power system. The settling time of the frequency has been investigated as a result of the load disturbance. Finally, a comparison was made between the traditional methods, Genetic algorithm and Ziegler-Nichols method, and honey badger algorithm. Upon comparison of the results, it is revealed that the proposed algorithm shows fast-damping steady-state deviations frequency with presence of step load disturbance.

关键词： EV battery load frequency control honey badger algorithm two-area power system

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SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期223卷

作者： Hu, Gang Zhong, Jingyu Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ North Carolina Pembroke NC 28372 USA

The honey badger algorithm (HBA) is a meta-heuristic optimization algorithm that simulates the foraging behavior of honey badgers. Since the algorithm is prone to premature convergence when solving complex optimization problems. To improve the overall optimization performance of the basic HBA, this paper develops a modified HBA named SaCHBA_PDN based on the Bernoulli shift map, piecewise optimal decreasing neighbor-hood, and horizontal crossing with strategy adaptation and applies it to solve the unmanned aerial vehicle (UAV) path planning problem. Firstly, the Bernoulli shift map is invoked to the HBA algorithm to change its initiali-zation process, thus increasing the diversity of the population and speeding up the convergence speed. Secondly, a new piecewise optimal decreasing neighborhood strategy (PODNS) is proposed to address the shortcomings of unbalanced convergence of the traditional optimal neighborhood strategy. The proposed PODNS increases the optimization efficiency of HBA and enhances the local search ability to avoid falling into the local optimum. Finally, a novel horizontal crossing with strategy adaptation is introduced to balance exploration and exploi-tation and enhance the global optimization ability. These strategies collaborate to enhance HBA in accelerating overall performance. The superiority of SaCHBA_PDN is comprehensively verified by comparing it with the original HBA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions and IEEE CEC2017 test suite, respectively. Experimental results show that SaCHBA_PDN has a better performance than other optimization algorithms. Furthermore, SaCHBA_PDN is used to solve a UAV path planning problem based on the threat source model and applied to circular and irregular obstacle scenarios as well as two-dimensional grid maps. Simulation results show that SaCHBA_PDN can obtain more feasible and efficient paths in different obstacle environments.

关键词： honey badger algorithm Global optimization Bernoulli shift map Piecewise optimal decreasing neighborhood Horizontal crossing with strategy adaptation UAV path planning

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