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Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2021年第1期2021卷 1-15页

作者： Feng, Siling Chen, Yinjie Zhai, Qianhao Huang, Mengxing Shu, Feng Hainan Univ Sch Informat & Commun Engn 58 Renmin Ave Haikou 570228 Hainan Peoples R China Hainan Univ Sch Sci 58 Renmin Ave Haikou 570228 Hainan Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South Chin 58 Renmin Ave Haikou 570228 Hainan Peoples R China

As the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA.

关键词： Mobile edge computing Computation offloading Grey wolf optimizer whale optimization algorithm

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Multi-Objective Optimal Placement and Sizing of Distribution Static Compensator in Radial Distribution Networks With Variable Residential, Commercial and Industrial Demands Considering Reliability

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IEEE ACCESS 2021年 9卷 46911-46926页

作者： Noori, Alireza Zhang, Yiming Nouri, Negar Hajivand, Mohammad Beijing Univ Technol Coll Elect Informat & Control Engn Beijing 100124 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Minist Sci Res & Technol Aerosp Res Inst Tehran *** Iran Islamic Azad Univ Borujerd Branch Young Researchers & Elite Club Borujerd *** Iran

This article presents an optimal placement and sizing of distribution static compensator (DSTATCOM) in radial distribution networks as multi-objective optimization with the objective of power loss reduction, voltage profile improvement and network reliability considering different variable demands using whale optimization algorithm (WOA). The WOA is inspired by the hunting behavior of humpback whales. The optimal location and size of DSTATCOM are considered as decision variables that are determined using the WOA. Network reliability is defined as energy not supplied (ENS) of the customers caused by line outages. The proposed method is implemented on IEEE 33-bus and Ahvaz 59-bus distribution networks. The optimal placement and sizing of DSTATCOM are investigated considering different residential, commercial and industrial loads, and also the effect of incorporating reliability is evaluated on power loss, minimum voltage and ENS. The simulation results show that the power loss and ENS are declined and also voltage profile is improved by determining the location and size of DSTATCOM in the networks in different loading conditions using the WOA. The obtained results demonstrate that considering reliability further reduces the ENS and more customer demand is met. The power loss decreases 21.60%, 30.71%, and 22.03% for residential, commercial and industrial loads in the 33-bus network and 26.86%, 31.94%, and 24.39% for the 69-bus network, respectively compared to base networks. Moreover, the performance of the WOA is compared with previous studies and the results indicate the superiority of the proposed method.

关键词： optimization Distribution networks Reliability Capacitors Reactive power Resource management algorithms Radial distribution network distribution static compensator optimal placement and sizing variable demand reliability whale optimization algorithm

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A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method

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RENEWABLE ENERGY 2021年 179卷 2198-2211页

作者： Nezhad, M. Majidi Heydari, A. Pirshayan, E. Groppi, D. Garcia, D. Astiaso Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy

Wind energy produced in near and offshore farms is constantly increasing, mainly thanks to the use of new technologies and the economic costs decrease. Data coming from satellites recently launched can be effectively used in near and offshore areas for wind speed mapping, and long and short-term analyses of wind regimes. Furthermore, the use of artificial intelligence methods is constantly increasing for predicting wind energy production. In this framework, a new forecasting model for wind speed assessment is presented integrating Sentinel satellite imagery analysis, in two phases using multi-sensor satellites, and machine learning methods. In the first step, wind speed and bathymetry have been analysed by means of sentinel-1 (S-1) and sentinel-2 (S-2) satellites images, respectively. Furthermore, a hybrid forecasting model has been proposed to assess and predict wind speed. The machine learning model consists of an integrated model using generalized regression neural network (GRNN) and the whale optimization algorithm (WOA). The new developed method has been then applied to assess wind energy potential around the Favignana island in Sicily, Italy. The results show that all the important primary parameters for a wind farm installations potential analysis, such as wind speed, water depth, and distance to the shoreline can be successfully analysed in a possible short time and freely. The following results have been obtained testing the new method: i) S-1 and S-2 satellite images have good potential for near and offshore wind speed assessment and bathymetry detection around Favignana island, ii) the root mean square error (RMSE), mean square error (MAE), and mean absolute percentage error (MAPE) of the proposed forecasting model using the whole dataset are 0.0205, 0.0159, and 6.8385 respectively, iii) the proposed forecasting model for wind speed has higher accuracy than other valid models. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind speed assessment Relative water depth Sentinel satellites family Generalized regression neural network whale optimization algorithm

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Improving Localization Success Rate of Three Magnetic Targets Using Individual Memory-Based WO-LM algorithm

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IEEE SENSORS JOURNAL 2021年第19期21卷 21750-21758页

作者： Lv, Bowen Qin, Yanding Dai, Houde Su, Shijian Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Jinjiang 362216 Peoples R China

Magnetic localization technique has the advantages of millimeter-level localization accuracy, fast localization speed, and no limit-of-sight constraints. However, most studies are limited to the tracking of single or two magnets because of the low localization success rate for multiple magnets, thereby restricting its application scenarios. In this study, a novel multi-magnetic target localization method, named individual memory-based whale optimization and Levenberg-Marquardt (IMWO-LM) algorithm, is proposed to overcome the problem of low localization success rate. The IMWO algorithm adds the individual historical optimal location memory to each whale, which improves the global search ability of the population. This IMWO algorithm result is employed as the initial guess of the LM method. Afterward, the LM result is adopted as the initial guess of the LM method in the subsequent localization process. In 20 groups of 500 localizations, the average localization success rates of the particle swarm optimization-LM algorithm, whale optimization-LM algorithm, and the proposed IMWO-LM algorithm were 85.1%, 83.82%, and 98.28%, respectively. In the experiment of locating three magnetic targets simultaneously, the average position error and the average orientation error were 3.46 mm and 6.77 degrees, respectively. Results show that this method can significantly improve the localization success rate while maintaining high localization accuracy.

关键词： Location awareness Magnetometers Magnetic flux whales Permanent magnets optimization Magnetic sensors Magnetic localization multi-target localization whale optimization algorithm individual memory

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Analysis of Third-Order Nonlinear Multi-Singular Emden-Fowler Equation by Using the LeNN-WOA-NM algorithm

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IEEE ACCESS 2021年 9卷 72111-72138页

作者： Zhang, Yin Lin, Jianqiang Hu, Zhenhuan Khan, Naveed Ahmad Sulaiman, Muhammad South China Inst Software Engn Dept Elect Guangzhou 510000 Peoples R China Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou 545000 Peoples R China Abdul Wali Khan Univ Dept Math Mardan 23200 Pakistan

In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden-Fowler equation (TONMS-EFE) using the strength of universal approximation capabilities of Legendre polynomials based Legendre neural networks supported with optimization power of the whale optimization algorithm (WOA) and Nelder-Mead (NM) algorithm. Unsupervised error functions are constructed in terms of mean square error for governing TONMS-EF equations of first and second order. Unknown designed parameters in LeNN structure are optimized initially by WOA for global search while NM algorithm further enhances the rapid local search convergence. The proposed algorithm's objective is to show the accuracy and robustness in solving challenging problems like TONMS-EFE. To study our designed scheme's performance and effectiveness, LeNN-WOA-NM is implemented on four cases of TONMS-EFE. The results obtained by the proposed algorithm are compared with the Particle Swarm optimization (PSO) algorithm, Cuckoo search algorithm (CSA), and WOA. Extensive graphical and statistical analysis for fitness value, absolute errors, and performance indicators in terms of mean, median, and standard deviations show the proposed algorithm's efficiency and accuracy.

关键词： Mathematical model whales optimization Approximation algorithms Sociology Shape Neural networks Singular Emden– Fowler equation soft computing algorithm weighted legendre neural networks Nelder-Mead algorithm whale optimization algorithm

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Machining Performance of Inconel 718 using WOA in PAC

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MATERIALS AND MANUFACTURING PROCESSES 2021年第11期36卷 1274-1284页

作者： Karthick, M. Anand, P. Meikandan, M. Siva Kumar, M. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Mech Engn 400 Feet Outer Ring RoadAvadi Chennai 600062 Tamil Nadu India

Machining superalloys through conventional methods is extremely difficult due to their inherent mechanical properties such as improved toughness, lower thermal conductivity, and strength-to-weight ratio. The paper deals with machining studies on Inconel 718, a nickel-based superalloy, through the plasma arc cutting (PAC) process. Response surface methodology -based Box-Behnken Design) approach was applied for developing and executing the PAC experiments. Statistical validation, parameter's influence on the responses, and mathematical relations between the responses and parameters were investigated through multiparameter analysis of variance. whale optimization algorithm was applied to perform the multiresponse optimization to identify the prime PAC parameters. The microstructure of cut surfaces was investigated through a scanning electron microscope to distinguish the cutting effects on substrate surface along with the several cutting conditions.

关键词： Plasma arc cutting inconel 718 optimization whale optimization algorithm Box– Behnken design

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Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

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EXPERT SYSTEMS 2023年第3期40卷

作者： Ranasingh, Soumyaranjan Pradhan, Tapan Dhenuvakonda, Koteswara Raju Natl Inst Technol Dept Elect Engn Silchar India

Calibration is the backbone of any sensor and measurement philosophy. The conventional calibration techniques for electrical parameter measurement using nonlinear sensors are affected by the repeating analogue signals and lead to errors in measurement. This research paper investigates different regression-based mathematical models to calibrate the Hall sensor for measuring RMS and the fundamental frequency. The novelty of this research work lies in the feature-based input modelling to measure RMS current and frequency with an error of 3.37e-12% and 7.61e-9%, respectively. The conventional Fourier transform method is compared with six different bio-inspired metaheuristic algorithms to estimate the frequency components of the analogue signal received from the measurement setup. Apart from the conventional sine waveforms, this paper investigates sawtooth and square waveforms as the periodic signals for determining the frequency components. The results from the comparison study show that the whale optimization algorithm exhibits 1.25% lesser error whilst predicting the features in measured frequency components. Apart from this, the paper identifies a unique combination of features that effectively measures the instantaneous electrical parameter with RMSE, NRMSE, and R2 value of 158.8, 0.51, and 0.44, respectively. Experimentally it is found that the decision tree and random forest regression models calibrate the Hall sensor with 93% less error than their linear and polynomial counterparts.

关键词： calibration current sensor decision tree regression frequency estimation regression models whale optimization algorithm

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On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

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REMOTE SENSING 2023年第14期15卷

作者： Zheng, Xiaomeng Zhang, Dehai Zhao, Jin Jiang, Maofei Chinese Acad Sci Natl Space Sci Ctr Key Lab Microwave Remote Sensing Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100040 Peoples R China

HY-2C is the third satellite in China's ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (T-B) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR's antenna temperatures and a model based on the whale optimization algorithm (WOA), Levenberg-Marquardt (LM) algorithm, and Back-Propagation neural network (WOA-LM-BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated T-B at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3.

关键词： microwave radiometer on-orbit calibration wet tropospheric correction whale optimization algorithm Back-Propagation neural network

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An embedded feature selection approach for depression classification using short text sequences

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Priya, S. Kavi Karthika, K. Pon Mepco Schlenk Engn Coll Dept Comp Sci & Engn Sivakasi 626005 Tamil Nadu India

Depression has become a serious mental health issue worldwide, particularly due to the rise of the Global Pandemic. Identifying depression of an individual from short texts shared in social media is a challenging task. The present work aims to select the optimal feature subset for classifying short texts for depression detection. By performing feature selection, it is possible to eliminate redundant and noisy features in high-dimensional datasets with small sample sizes. This can prevent the "curse of dimensionality" and enhance the effectiveness of classification algorithms. However, current feature selection methods often focus on optimizing classification or clustering performance, while neglecting the stability of the selected features. This can lead to unstable results and make it challenging to identify meaningful and interpretable features. This paper introduces a novel embedded feature selection approach named Statistical Relevance Class Frequency based on whale optimization algorithm (SRCF-WOA) for selecting feature subsets from short texts in social media. The proposed methodology extracts both the unigram features and composite features to capture the semantic and structural information. chi *** (Chi-squared relevance class frequency) filter approach is applied to rank the extracted features to signify the importance of the features. WOA is adapted to retrieve the optimal subset of features with low-dimensional space using its high exploration and high exploitation capability. In the evaluation process, four benchmark short text datasets and two classifiers are used. The comparison shows that the proposed embedded feature selection method outperforms other algorithms in terms of accuracy and F beta scores(beta = 0.5, 1, and 2). The sensitivity analysis is carried out to check the robustness and stability of the proposed method. The findings indicate that the SRCF-WOA surpasses other methods on the majority of datasets, achieving the maximum classificati

关键词： Depression detection Composite feature selection Dimensionality reduction whale optimization algorithm Text classification

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Optimized Deep Neuro-Fuzzy Network with MapPeduce Architecture for Acute Lymphoblastic Leukemia Classification and Severity Analysis

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2024年第3期24卷

作者： Bai, G. Mercy Venkadesh, P. Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Tamil Nadu India VSB Coll Engn Dept CSE Tech Campus Coimbatore India

The most common life-threatening disease, acute lymphoblastic leukemia (ALL), can be lethal within a few weeks if untreated. The early detection and analysis of leukemia is a key dilemma in the field of disease diagnosis, and the methods available for the classification process are time-consuming. To overcome the issues, this paper develops a robust classification technique named Horse Herd whale optimization-enabled Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for ALL classification and severity analysis using the MapReduce framework. The input image is first preprocessed and segmented, and the useful features necessary for improving the classification performance are extracted during the mapper phase, known as HHWO, which incorporates Horse Herd optimization algorithm (HOA) and whale optimization algorithm (WOA). Finally, severity analysis of ALL is done to classify the levels of leukemia to offer optimal treatment. As a result, the developed method performed better than other existing methods, achieving superior performance with a greater testing accuracy of 0.959, sensitivity of 0.965, and specificity of 0.966, respectively.

关键词： Acute lymphoblastic leukemia severity analysis deep neuro-fuzzy network Horse Herd optimization whale optimization algorithm

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