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A new QoS-aware method for production scheduling in the industrial internet of things using elephant herding optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3611-3626页

作者： Avval, Danial Bakhshayeshi Heris, Pouria Ouni Navimipour, Nima Jafari Mohammadi, Behnaz Yalcin, Senay Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Kadir Has Univ Dept Comp Engn Istanbul Turkey Nisantasi Univ Dept Comp Engn TR-34485 Istanbul Turkey

The Internet of Things (IoT) is a network of physical items implanted with software, sensors, etc., to link and exchange data with other devices. These devices vary in complexity from common household items to sophisticated industrial instruments. It would be challenging to choose an appropriate IoT service based on the requirements of the vast pool of accessible services with similar capabilities, given the growth of IoT-based service providers in the market. A suitable selection may be made using quality-of-service (QoS) parameters that characterize a service. IoT has several benefits over traditional communication systems. Also, it is a component of a safe and smart city system known as the Industrial Internet of Things (IIoT) which is particularly useful in the industrial field. However, it suffers from various issues such as high costs, energy consumption, and long delays. The production scheduling problem is one of the main issues in IIoT, and it is an NP-hard problem regarding cost and energy efficiency. Therefore, a meta-heuristic algorithm based on the elephant herd optimization algorithm is proposed to minimize resource costs, conversion costs, and the cost of continuous development delays. By combining the clan updating factor, separating operator, and the proposed algorithm, we created an effective and efficient method to solve the issue of production scheduling. Many experiments are performed to determine the performance of industrial environments. The outcomes demonstrate that the suggested technique can optimize planning and achieve cost reduction, efficient energy consumption, and latency decrease.

关键词： Industry Internet of things elephant herding optimization algorithm Production scheduling Energy Cost

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Static Drone Placement by elephant herding optimization algorithm 25

Static Drone Placement by Elephant Herding Optimization Algo...

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25th Telecommunication Forum (TELFOR)

作者： Strumberger, Ivana Bacanin, Nebojsa Tomic, Slavisa Beko, Marko Tuba, Milan Megatrend Univ Grad Sch Comp Sci Bulevar Umetnosti 29 Belgrade 11070 Serbia IST ISR Ave Rovisco Pais 1 P-1049001 Lisbon Portugal Univ Lusofona Humanidades & Tecnol Campo Grande 376 P-1749024 Lisbon Portugal Univ Nova Lisboa CTS Campus FCT UNL P-2829516 Monte De Caparica Caparica Portugal State Univ Novi Pazar Dept Tech Sci Novi Pazar Serbia

ISBN: (纸本)9781538630730

Optimal placement of drones is a very challenging problem and it belongs to the group of hard optimization problems for which swarm intelligence algorithms were successfully applied. This paper presents an implementation of the recent elephant herding optimization algorithm for solving the static drone location problem. The objective of the model applied in this paper is to establish monitoring of all targets with the least possible number of drones. In empirical tests we used two problem instances: one with 30 uniformly distributed targets, and one with 30 clustered targets. The simulation results show that the elephant herding optimization algorithm performs well in covering targets for both instances of the problem, especially considering the number of drones that were deployed.

关键词： elephant herding optimization algorithm meta-heuristic optimization static drone location problem swarm intelligence

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Premature Infant Cry Classification via elephant herding Optimized Convolutional Gated Recurrent Neural Network

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第10期43卷 6592-6619页

作者： Vaishnavi, V. Braveen, M. Muthukumaran, N. Poonkodi, P. Sethu Inst Technol Dept Elect & Elect Engn Virudunagar 626115 India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamilnadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India SNS Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Premature babies scream to make contact with their mothers or other people. Infants communicate via their screams in different ways based on the motivation behind their cries. A considerable amount of work and focus is required these days to preprocess, extract features, and classify audio signals. This research aims to propose a novel elephant herding Optimized Deep Convolutional Gated Recurrent Neural Network (EHO-DCGR net) for classifying cry signals from premature babies. Cry signals are first preprocessed to remove distortion caused by short sample times. MFCC (Mel-frequency cepstral coefficient), Power Normalized Cepstral Coefficients (PNCC), BFCC (Bark-frequency cepstral coefficient), and LPCC (Linear Prediction cepstral coefficient) are used to identify abnormal weeping through their prosodic aspects. The elephant herding optimization (EHO) algorithm is utilized for choosing the best features from the extracted set to form a fused feature matrix. These characteristics are then used to categorize premature baby cry sounds using the DCGR net. The proposed EHO-DCGR net effectiveness is measured by precision, specificity, recall, and F1-score, accuracy. According to experimental fallouts, the proposed EHO-DCGR net detects baby cry signals with an astounding 98.45% classification accuracy. From the experimental analysis, the EHO-DCGR Net increases the overall accuracy by 12.64%, 3.18%, 9.71% and 3.50% better than MFCC-SVM, DFFNN, SVM-RBF and SGDM respectively.

关键词： Premature infant cry Deep learning Fused feature matrix Classification elephant herding optimization algorithm

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Hybrid optimization assisted transmit antenna selection for massive MIMO technology

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第7期83卷 20909-20929页

作者： Singh, Charanjeet Raja, P. C. Kishore Deenbandhu Chhotu Ram Univ Sci & Technol Elect & Commun Dept Sonipat Haryana India Vellore Inst Technol VIT Vellore 632014 India

MIMO (Multi-input, Multi-output) systems are a key technology for achieving 5G wireless communications' performance goals. They achieve significant levels of various acquisition, consistency, and efficiency. Nonetheless, for more transmit antennas;more radio frequency (RF) chains are necessary, which may result in increased computational complexity and expenses. As a result, selecting the optimal transmit antennas is critical for improving system performance. This paper offers a new Fitness-based elephant herding algorithm (F-EHA) for selecting the optimal broadcast antenna by specifying many criteria such as secrecy rate and efficiency. In addition, the system provides a recommendation for which antenna to use. Finally, the suggested model's advantage over existing techniques in terms of EE (Energy Efficiency) and secrecy rate is demonstrated.

关键词： Multi-input Multi-output Secrecy Rate Energy Efficiency elephant herding optimization algorithm Fitness-based elephant herding algorithm

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Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期82卷 20741-20770页

作者： Kollem, Sreedhar Prasad, Ch Rajendra Ajayan, J. Malathy, V Subbarao, Akkala SR Univ Sch Engn Dept ECE Warangal Telangana India Maulana Azad Natl Inst Technol Dept ECE Bhopal India

Because of the variety of shapes, locations, and image intensities, image segmentation is a more difficult endeavor in image processing. The most frequent diseases in the world are brain tumors. Therefore, brain tumor segmentation is essential in the medical field. The objective of this work is to propose image denoising and segmentation algorithms, as image segmentation is highly dependent on image denoising. The proposed image denoising algorithm is included in this article as part of the pre-processing stage. For image denoising, a novel adaptive diffusivity function based on partial differential equations is implemented. The diffusivity function's purpose is to enhance the images of brain tumors using a gradient, a Laplacian, and an adaptive threshold while also preserving image details. For image segmentation, the enhanced image is fed into an improved multi-kernel fuzzy c-means method, which then optimizes the centroid using an elephant herding optimization algorithm. Finally, it differentiates between tumor and non-tumor tissue. Images from the BRATS2020 Database were used to assess the effectiveness of the proposed approaches. When compared to conventional techniques, the proposed method performs well and proves to be an effective technique (PSNR-39.4001dBs, SSIM-99.78%, and MSE-7.4656 for image denoising;Sensitivity-98.45%, specificity-99.87%, and accuracy-99.83% for image segmentation at sigma= 20). The proposed method is developed and demonstrated in the MATLAB environment.

关键词： Partial differential equation Diffusivity function Multi-kernel fuzzy c-means method elephant herding optimization algorithm Adaptive threshold

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Method for Fault Diagnosis and Speed Control of PMSM

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Computer Systems Science & Engineering 2023年第6期45卷 2391-2404页

作者： Smarajit Ghosh Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering&TechnologyPatialaPunjabIndia

In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and *** tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault *** has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial *** this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is *** Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous *** the fault location and fault severity are *** this,eccentricity fault may occur in the *** control the speed of the permanent magnet synchronous motor,Dolphin Swarm optimization(DSO)algorithm is *** proposed work is simulated by using MATLAB in terms of amplitude,speed and *** comparison graph of speed *** obtained by the proposed method gives better result compared to the other existing *** proposed work is also compared with Particle Swarm optimization(PSO)and elephant herding optimization(EHO)*** proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.

关键词： Amplitude electricmotor elephant herding optimization algorithm fault detection partial swarm optimization algorithm permanent magnet synchronous motor

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Attack detection and prevention in IoT-SCADA networks using NK-classifier

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SOFT COMPUTING 2022年第14期26卷 6811-6823页

作者： Justindhas, Y. Jeyanthi, P. Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India

Supervisory control and data acquisition (SCADA) stands as a control system consisting of computers and networked data communications. At present, many industries use SCADA to monitor as well as control the processes. In recent days, numerous attacks are targeting these systems. Thus, the furtherance of high-security SCADA is much-needed one on account of its susceptibility to attacks centered on the architectural restriction. To identify these attacks, numerous classifications, optimization methods, and intrusion detecting systems (IDS) are posited. The chief drawbacks of this prevailing work are detecting accuracy, high training time, and security. For prevailing over these disadvantages, an NK-RNN classifier is proposed to recognize the intrusions in the SCADA method. Initially, the features from the datasets are organized, and the important attributes are chosen by utilizing the elephant herding optimization (EHO). Secondly, the data, which is optimized, are grouped and classified by applying the NK-RNN classifier. Then, the outcomes, which are classified, are assessed and utilized to outcome prediction. In normal data, Caesar Ciphering is employed for the prevention of attacks and also the modified elliptic curve cryptography is employed for enhancing the security level. From the performance assessment, it is revealed that the NK-RNN method attains superior performance than the prevailing classification method along with IDS algorithms.

关键词： Supervisory control and data acquisition Attack detection Intrusion detection system elephant herding optimization algorithm Feature selection Normalized K-means clustering algorithm

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Application of IEHO-BP neural network in forecasting building cooling and heating load

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ENERGY REPORTS 2022年 8卷 455-465页

作者： Wang, Hai-Jun Jin, Tao Wang, Hui Su, Dan Ordos Inst Technol Dept Math & Comp Engn Ordos 017000 Inner Mongolia Peoples R China Ordos Inst Technol Dept Management Ordos 017000 Inner Mongolia Peoples R China

Countering the issue of low optimization accuracy and poor stability of the elephant herding optimization (EHO) algorithm when solving multi-dimensional nonlinear complex problems, putting forward an improved elephant herding optimization (IEHO) algorithm. The algorithm improves the accuracy of EHO algorithm optimization by chaosing the initial solution, adding dynamic influence factors, Levy flight operators and boundary mutation operators in the position update process. Standard functions are used for test experiments, and the results indicate that the introduction of improved strategies can effectively improve the accuracy and stability of the EHO algorithm when solving optimization problems. In view of the performance of the IEHO algorithm in function optimization, combining it with the BP neural network, proposing the IEHO-BP neural network algorithm, and using new algorithm to forecasting the building cooling and heating load. The experimental results show that compared with other group intelligence optimization algorithms, the output results of the cooling and heating load forecasting model based on the IEHO-BP neural network algorithm are more accurate and less oscillating. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Load forecasting BP neural network elephant herding optimization algorithm Weight and threshold optimization Swarm intelligence

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Improving convergence in swarm algorithms by controlling range of random movement

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NATURAL COMPUTING 2021年第3期20卷 513-560页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

Swarm intelligence algorithms are stochastic algorithms, i.e. they perform some random movement. This random movement imparts the algorithms with exploration capabilities and allows them to escape local optima. Exploration at the start of execution helps with thorough inspection of the search/solution space. However, as the algorithm progresses, the focus should ideally shift from exploration to exploitation. This shift would help the algorithm to enhance existing solutions and improve its convergence capabilities. Hence if the range of random movement is not kept in check, it may limit an algorithm's convergence capabilities and overall efficiency. To ensure that the convergence of an algorithm is not compromised, an improved search technique to reduce range of uniform random movement was recently proposed for bat algorithm. Uniform distribution and levy distribution are the most commonly used random distributions in swarm algorithms. In this paper, the applicability of the improved search technique over different swarm algorithms employing uniform and levy distributions, as well as Cauchy distribution has been studied. The selected algorithms are firefly algorithm, cuckoo search algorithm, moth search algorithm, whale optimization algorithm, earthworm optimization algorithm and elephant herding optimization algorithm. The resultant variants of each of these algorithms show improvement upon inclusion of the improved search technique. Hence results establish that the improved search technique has positive influence over swarm algorithms employing different random distributions.

关键词： Exploitation Improved search Moth search algorithm Cuckoo search algorithm Firefly algorithm Whale optimization algorithm Earthworm optimization algorithm elephant herding optimization algorithm

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Optimal Designing and Synthesis of a Hybrid PV/Fuel cell/Wind System using Meta-heuristics

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ENERGY REPORTS 2020年 6卷 1353-1362页

作者： Cao, Yan Yao, Hui Wang, Zhijie Jermsittiparsert, Kittisak Yousefi, Nasser Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China Xian Technol Univ Shaanxi Key Lab Nontradit Machining Xian 710021 Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Islamic Azad Univ Karaj Branch Tehran Iran

In this paper, a multi-objective optimization method has been established on a hybrid PV, wind, fuel cell, and battery system. The optimization is based on three models including energy supply reliability, electricity efficiency, and capital cost of the hybrid system. A new model of the elephant herding optimization (BEHO) algorithm is utilized to solve the multi-objective optimization problem and is validated based on different algorithms and benchmark functions. The main purpose is to determine the Pareto surface including a set of possible design solutions to help the decision-makers obtaining the global optimum solution. The final results indicated that the proposed method is an applicable approach for designing of the proposed hybrid system. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Hybrid system Lithium-ion battery Proton exchange membrane fuel cell PV Wind elephant herding optimization algorithm Levy flight

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