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Vector quantization using whale optimization algorithm for digital image compression

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 20077-20103页

作者： Rahebi, Javad Istanbul Ayvansaray Univ Dept Software Engn Istanbul Turkey

Today, much of the information is storing in images. To transfer information in the form of images, image compression is required. Compressing images reduces the size of images and sends them faster over the network. One of the most methods of image compression is the vector quantization. For vector quantization compression, the codebook is using in cryptography and decryption. The vector quantization compression method typically uses codebooks that are not optimized, which reduces the compression quality of the images. Choosing the optimal codebook makes compression of images with higher quality. Choosing the optimal codebook is a difficult optimization problem and therefore requires intelligent algorithms to solve it. In this paper, the whale optimization algorithm is used to find the optimal codebook in image compression. whale optimization algorithm has different search strategies and is an ideal algorithm for finding the optimal codebook in images compression. Implementation of the proposed algorithm for compression on several standard images shows that the proposed method compresses images with appropriate quality. The proposed method performs more efficient compression than the proposed algorithms such as particle swarm optimization, bat, and firefly algorithms. The signal-to-noise ratio of the proposed method is higher than the compared methods. Experiments on a set of standard images show the proposed method compared to the Fire Fly, Bat, and Differential evolution, Improved Particle Swarm optimization, and Improved Differential Evolution methods with a compression execution time of 60.48% and 10.21, respectively. %, 4.79%, 5.09% and 3.94% decreased. The proposed method in compression has a higher PSNR index of about 17% than the Linde-Buzo-Gray method.

关键词： Image compression Image processing Vector quantization whale optimization algorithm

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Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2023年第1期2023卷 13页

作者： Vinaykumar, V. N. Babu, J. Ananda Frnda, Jaroslav Malnad Coll Engn Dept Informat Sci & Engn Hassan Karnataka India Univ Zilina Fac Operat & Econ Transport & Commun Dept Quantitat Methods & Econ Informat Zilina Slovakia Univ Zilina Univ Sci Pk Zilina Slovakia

Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet-ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA-Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset.

关键词： AlexNet Bi-directional long short-term memory Convolutional neural network Optimal guidance ResNet50 Satellite image classification whale optimization algorithm

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A novel equation for longitudinal dispersion coefficient prediction based on the hybrid of SSMD and whale optimization algorithm

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年第0期716卷 137007-000页

作者： Memarzadeh, Rasoul Zadeh, Hossein Ghayoumi Dehghani, Majid Riahi-Madvar, Hossien Seifi, Akram Mortazavi, Seyed Mostafa Vali E Asr Univ Rafsanjan Fac Engn Dept Civil Engn POB 518 Rasanjan Iran Vali E Asr Univ Rafsanjan Fac Engn Dept Elect Engn POB 518 Rasanjan Iran Vali E Asr Univ Rafsanjan Fac Engn Dept Water Engn POB 518 Rasanjan Iran

The purpose of the present paper is improving the accuracy of existing formulas for the longitudinal dispersion coefficient (LDC) prediction based on a novel and simple meta-heuristic optimization method called whale optimization algorithm (WOA). Although several existing formulas calculate LDC in the rivers based on the hydraulic and hydrodynamic specifications, most of them have significant errors in confronting extensive field data. In this study, comprehensive field data, including the geometrical and hydraulic properties of different rivers in the world, were adopted to build a reliable model. Statistical error measures were used to evaluate and compare the results with other studies. Furthermore, the Subset Selection of Maximum Dissimilarity (SSMD) method was utilized for a reputable selection of data for training and testing the WOA model. Subset selection is a critical factor in artificial intelligence (Al) computations. Finally, an integrated model based on the SSMD method and WOA technique has been proposed to develop the high accuracy formulas for the prediction of LDC. According to the results, the developed formulas are competitive or superior to the previous formulas for LDC estimation. Results also indicated that the WOA algorithm could be applied to improve the performance of the predictive equations in other fields of studies by finding the optimum values of coefficients. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Longitudinal dispersion coefficient whale optimization algorithm Subset selection of maximum dissimilarity method Natural rivers

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Performance improvement of photovoltaic power systems using an optimal control strategy based on whale optimization algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2018年第Apr.期157卷 168-176页

作者： Hasanien, Hany M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

Photovoltaic (PV) installations are consistently increasing all over the world, leading to a high penetration to the electric grid. Tremendous efforts should be exerted to maintain the operation of the PV systems at optimal conditions. This paper introduces an optimal control strategy with the purpose of enhancing the performance of PV systems. This control strategy is based on the proportional-integral (PI) controller, which is designed by using the whale optimization algorithm (WOA). The response surface methodology (RSM) model is established to create the objective function and its constraints. The proposed WOA-based PI controllers are utilized to control the DC chopper and grid-side inverter in order to achieve a maximum power point tracking operation and improve the dynamic voltage response of the PV system, respectively. The effectiveness of the control strategy is tested under different operating conditions of the PV system such as (1) subject the system to symmetrical and unsymmetrical fault conditions, (2) study the system responses under different irradiation and temperature conditions using real data extracted from a field test, and (3) subject the system to a sudden load disturbance in an autonomous operation. This effectiveness is compared with that achieved using the generalized reduced gradient (GRG) algorithm based PI controller. The validity of the proposed control strategy is extensively verified by the simulation results, which are performed using PSCAD/EMTDC environment. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Photovoltaic power systems Power system control and dynamics Response surface methodology whale optimization algorithm

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GWOA: a hybrid genetic whale optimization algorithm for combating attacks in cognitive radio network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第11期10卷 4345-4360页

作者： Elghamrawy, Sally M. Hassanien, Abou Ella MISR Higher Inst Engn & Technol Mansoura Egypt SRGE Cairo Egypt Cairo Univ Fac Comp & Informat Cairo Egypt

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users' (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic whale optimization algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

关键词： Securing spectrum sensing Primary user emulation PUE Cognitive radio network (CRN) Genetic algorithm whale optimization algorithm

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An Optimal Node Localization in WSN Based on Siege whale optimization algorithm

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2201-2237页

作者： Thi-Kien Dao Trong-The Nguyen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Multimedia Communication Lab University of Information TechnologyHo Chi Minh CityVietnam

Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.

关键词： Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization

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3DPCNN based on whale optimization algorithm for color image segmentation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第1期37卷 1499-1511页

作者： Xing, Zhikai Jia, Heming Song, Wenlong Northeast Forestry Univ Harbin Heilongjiang Peoples R China

Considering that the 3D pulse-coupled neural network (3D-PCNN) model has the deficiency of high parameter complexity and low accuracy in color image segmentation, swarm intelligence optimization algorithm is adopted to optimize the image segmentation process. In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and beta. The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate.

关键词： 3D-PCNN color image segmentation whale optimization algorithm improved product cross entropy

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A Location Method for Partial Discharge Using Time Reversal and Improved whale optimization algorithm

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IEEE ACCESS 2020年 8卷 171977-171987页

作者： Ma, Pengchi Wang, Zhijie Liu, Yonghui Shen, Pan Wang, Hong Fu, Xiaolin Yang, Wanhao Shanghai Dianji Univ Coll Elect Engn Shanghai 201306 Peoples R China Jiangsu Hongyuan Elect Co Ltd Nanjing 211100 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

Time difference of arrival method is commonly used in the location of partial discharges. In this method, the main problem are as follows: (1) time delay error is inevitable. (2) location algorithm is sensitive to time delay error. To solve the problems above, an ultra-high frequency localization method for transformer partial discharge based on time reversal and improved whale optimization algorithm is proposed in this article. The method is consisted of three parts, that including time delay extraction, time reversal imaging and optimal solution. Firstly, the time delay is extracted by energy accumulation method. And then the approximate position of partial discharge source is determined by time reversal imaging. Combined with the time reversal imaging, the strategy of initial population selection and weight setting in the whale optimization algorithm is improved. Finally, the improved whale optimization algorithm is used to solve the optimal solution. The results are demonstrated through simulation and experiment. Compared with other localization algorithms, it is shown that the improved whale optimization algorithm has a better control performance in stability and accuracy.

关键词： Partial discharges whales optimization Delay effects Focusing Interference Transformer partial discharge ultra-high frequency time reversal whale optimization algorithm location method

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Exhaust emission reduction of a SI engine using acetone-gasoline fuel blends: Modeling, prediction, and whale optimization algorithm

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ENERGY REPORTS 2023年 9卷 77-86页

作者： Alahmer, Hussein Alahmer, Ali Alkhazaleh, Razan Alrbai, Mohammad Al Balqa Appl Univ Fac Artificial Intelligence Dept Automated Syst Al Salt 19117 Jordan Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Tafila Tech Univ Fac Engn Dept Mech Engn POB 179 Tafila 66110 Jordan Univ Jordan Sch Engn Dept Mech Engn Amman 11942 Jordan

Acetone-gasoline fuel is one potential favorable alternative option to regular gasoline fuel. This work attempts to mitigate pollution while simultaneously improving SI engine performance with acetone (AC) proportion in fuel mixture and varying engine speed. Acetone-gasoline alternative fuel was experimentally prepared by mixing 5-10 vol.% of acetone into ordinary gasoline. The experiments were performed in a spark ignition (SI) single-cylinder, four-stroke at variable engine speed. The engine performance was measured by an eddy current dynamometer (ECD), while the exhaust engine was measured by a gas analyzer connected to the gasoline engine. The proposed strategy, the integrated combined polynomial regression with whale optimization algorithm (WOA), was implemented. The results showed that the acetone blending of 10 % with gasoline (AC10: 10 vol. % acetone + 90 vol. % gasoline) decreases carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxides (NOx), emissions by 26.3%, 30.3%, and 6.6%, respectively. Furthermore, the AC10 exhibited better engine brake power (BP) than pure gasoline, with an improvement of 4.39%. According to the optimization results, the BP was enhanced by AC10 to record 2.426 kW at 2887 rpm. In addition, engine emissions reduced to the lowest levels of 142.849 ppm, 0.426%, and 1088.178 ppm, in terms of UHC, CO, and NOx, respectively. The experiment results show that the WOA optimizer can accurately predict the optimal point in SI engine performance and emissions. (c) 2022 The Author(s). 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： Acetone-gasoline blend SI engine Engine performance Emission control whale optimization algorithm Polynomial regression

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Seismic Response Prediction and Velocity Model Building Inversion by the whale optimization algorithm

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PURE AND APPLIED GEOPHYSICS 2023年第6期180卷 2087-2109页

作者： Routa, Alok Kumar Mohanty, Priya Ranjan Indian Inst Technol Indian Sch Mines Dept Appl Geophys Dhanbad 826004 India

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm that is based on the bubble-net hunting strategy used by humpback whales. The objective of this study is to assess the utilization of the WOA to perform the inversion of seismic data. The primary focus of this optimization is to first target an objective function for inversion that leads to minimizing the RMS error between field observed data and whale predicted data. The algorithm maintains a balance between exploration and exploitation phases that results in a better solution to the desired problem. The application of this technique to 12 benchmark models and validation through a true Vp model helps to reconstruct P-wave velocity and true model parameter estimation. The performance of the algorithm is compared with the gray wolf optimization (GWO) algorithm. The experimental results show that the final model after a reasonable number of iterations is able to provide optimum solutions within 10 different randomly generated search populations. As a consequence, it can be stated that the approach has higher reliability to reveal the P-wave imaging with a good convergence rate and minimum uncertainty over any complex geology.

关键词： Inversion Meta-heuristic algorithm RMS error Velocity model whale optimization algorithm

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