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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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An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

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ENERGY REPORTS 2020年 6卷 530-542页

作者： Cao, Yan Li, Yiqing Zhang, Geng Jermsittiparsert, Kittisak Nasseri, Maryam Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Islamic Azad Univ Dept Engn Ardabil Branch Ardebil Iran

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Proton exchange membrane fuel cell Parameter estimation LQR whale optimization algorithm Chaotic

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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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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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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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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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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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Chaotic whale optimization algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2018年第3期5卷 275-284页

作者： Kaur, Gaganpreet Arora, Sankalap DAV Univ Jalandhar Punjab India

The whale optimization algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algorithms, the main problem faced by WOA is slow convergence speed. So to enhance the global convergence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： whale optimization algorithm Meta-heuristic algorithm Chaos Chaotic maps

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Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 122卷 673-691页

作者： Miao, Yonghao Zhao, Ming Makis, Viliam Lin, Jing Beihang Univ Sch Reliabil & Syst Engn Xueyuan Rd 37 Beijing 100083 Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Since multicomponent modulation and complicated interference simultaneously exist in the vibration signals caused by the bearing compound fault, the fault feature becomes rather weak and is hard to be extracted. Therefore, the diagnosis of bearing compound fault is always considered as the bottle neck issue of machinery condition monitoring. The decomposition methods which can separate the individual component into different modes with appropriate criterion provide an alternative for solving this issue. However, various weaknesses, including mode mixing, depending on predefined mode number and poor noise suppression, seriously restrict their application range. To overcome the disadvantages, a novel decomposition method, swarm decomposition (SWD), is initially introduced in the diagnosis of the machinery fault. Inspired by the swarm intelligence, SWD can intelligently decompose the signal by using the swarm filtering and iterative algorithm. A series of analysis, including an impulse responses test and a numerical validation, verifies SWD has a remarkable performance in the decomposition of multicomponent modulation signal. Yet, the superiority also relies on the choice of thresholds. Therefore, a novel nature-inspired meta-heuristic optimization algorithm, whale optimization algorithm (WOA), is applied to solve the problem. Benefiting from the virtues of SWD and WOA, the proposed optimal swarm decomposition (OSWD) is more suitable for the weak feature extraction from multicomponent modulation signal. Finally, to further highlight its superiority in the diagnosis of the bearing compound fault, a number of simulation and real datasets from the axle box bearings of locomotive are applied. Results show that OSWD can be considered as an alternative to address the bottle neck issue from the bearing compound fault. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Swarm decomposition whale optimization algorithm Variational mode decomposition Bearing fault diagnosis Compound fault diagnosis Feature extraction

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