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Vehicular communication using federated learning empowered chimp optimization (FLECO) algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第29期83卷 72321-72356页

作者： Gupta, Ruchi Alam, Tanweer Abdul Kalam Tech Univ Ajay Kumar Garg Engn Coll Ghaziabad Fac Informat Technol Lucknow India Islamic Univ Madinah Fac Comp & Informat Syst Madinah Saudi Arabia

Recently in the field of vehicular communication, there has been a concentration of research on the integration of a vehicle-to-vehicle (V2V) network. With vehicle-to-vehicle (V2V) communication, users can directly exchange significant information with nearby vehicles. Typically, automobiles tend to travel at higher speeds on highways compared to roads at intersections. As a result, it is necessary to have a reliable system in place that can effectively and securely facilitate communication. In recent times, scientists have developed different methods for distributing information. However, these systems have various issues such as latency, reliability, mobility, and communication cost. Consequently, this results in a lack of dependability for real-time communication. Therefore, this study introduces a novel approach to Federated Learning (FL) by including the chimp optimization algorithm (ChOA). Federated Learning is an approach in the field of machine learning that enables multiple devices or nodes to collaboratively train a model without the need for data exchange. In the area of vehicular communication, utilization of Federated Learning can be employed to develop a predictive model that estimates the trajectory of nearby vehicles by utilizing collected data. The chimp optimization algorithm (ChOA) is designed to improve the model's efficacy. The proposed method aims to enhance the accuracy of the model's predictions regarding the conduct of nearby vehicles, while also reducing the amount of data exchanged between vehicles, by combining Federated Learning and chimp optimization termed FLECO. This method has the potential to enhance vehicular communication effectiveness and security, while also improving road safety and traffic management. Federated Learning facilitates the group control of a machine learning (ML) system by vehicles through the adjustment of model parameters. To enhance the energy efficiency of the system, the implementation of resource allocation

关键词： Federated learning Vehicle communication chimp optimization algorithm SUMO FLECO

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chimp optimization algorithm with Deep Learning-Driven Fine-grained Emotion Recognition in Arabic Corpus

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Wala bin Subait Iman Basheti Hanan Al Sultan Muhammad Alzaidi Jawhara Aljabri Mohammed Assiri Nahla Salih Department of Language Preparation Arabic Language Teaching Institute Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Riyadh Saudi Arabia The University of Sydney NSW Australia Sydney Australia Department of English College of Arts King Faisal University Saudi Arabia Al-Ahsa Saudi Arabia Department of English Language College of Language Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science University College in Umluj University of Tabouk Tabuk Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more than other languages. Emotion detection is a major challenge in Natural Language Processing (NLP), which allows machines to ascertain the emotions expressed in the text. The task includes recognizing and identifying human feelings such as fear, anger, sadness, and joy. The discovered sentiments and feelings expressed in tweets have gained much recognition in recent years. The Arab region has played a substantial role in international politics and the global economy needs to scrutinize the emotions and sentiments in the Arabic language. Lexicon-based and machine-learning techniques are two common models that address the problems of emotion classification. This study introduces a chimp optimization algorithm with a Deep Learning-Driven Arabic Fine-grained Emotion Recognition (COADL-AFER) technique. The presented COADL-AFER technique mainly aims to detect several emotions in Arabic tweets. In addition to its academic significance, the COADL-AFER technique has practical applications in various fields, including enhancing applications of E-learning, aiding psychologists in recognising terrorist performance, improving product quality, and enhancing customer service. The COADL-AFER technique applies the long short-term memory (LSTM) model for emotion detection. Finally, the hyperparameter selection of the LSTM method can be accomplished by COA. The experimental validation of the COADL-AFER system, a crucial step in our research, is verified utilizing the Arabic tweets dataset. The simulation results stated the betterment of the COADL-AFER technique, further reinforcing the reliability of our research.

关键词： Emotion Recognition Deep Learning Natural Language Processing Arabic Language chimp optimization algorithm

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Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification

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SCIENTIFIC REPORTS 2024年第1期14卷 1-32页

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The chimp optimization algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence rate. However, CHoA has a weak exploration capability and tends to fall into local optimal solutions in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. To solve this problem, this paper proposes the Enhanced chimp Hierarchy optimization algorithm for adaptive lens imaging (ALI-CHoASH) for searching the optimal classification problems for the optimal subset of features. Specifically, to enhance the exploration and exploitation capability of CHoA, we designed a chimp social hierarchy. We employed a novel social class factor to label the class situation of each chimp, enabling effective modelling and optimization of the relationships among chimp individuals. Then, to parse chimps' social and collaborative behaviours with different social classes, we introduce other attacking prey and autonomous search strategies to help chimp individuals approach the optimal solution faster. In addition, considering the poor diversity of chimp groups in the late iteration, we propose an adaptive lens imaging back-learning strategy to avoid the algorithm falling into a local optimum. Finally, we validate the improvement of ALI-CHoASH in exploration and exploitation capabilities using several high-dimensional datasets. We also compare ALI-CHoASH with eight state-of-the-art methods in classification accuracy, feature subset size, and computation time to demonstrate its superiority.

关键词： Feature selection chimp optimization algorithm Hierarchy Social class factor Local optimal

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The optimization of permanent magnet synchronous motor using chimp algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第3期44卷 290-302页

作者： Knypinski, Lukasz Poznan Univ Tech Inst Elect Engn & Elect Poznan Poland

PurposeThe purpose of this paper is to develop an algorithm and computer script for the optimization of permanent magnet synchronous machines with self-starting ***/methodology/approachThe optimization script consists of two independent modules: (a) optimization procedure and (b) a finite element models describing the investigation of line-start permanent magnet synchronous motors (LSPMSM). The proposed modification of the chimp optimization algorithm improves the convergence and reliability of the optimization procedure. The multiobjective compromise function consists of three components describing the functional parameters of the optimized machine. The field-circuit mathematical model of the dynamics operation of the LSPMSM was elaborated in Ansys and consists of: transient electromagnetic field equations, equations describing external electric circuits and mechanical motion *** proposed optimization procedure containing the chimp algorithm can be applied to the optimal selection of the selected parameters of the various permanent magnet motors. The optimization algorithm was modified to obtain better convergence. The proposed modification involves the use of different types of changes in the parameter characteristic of the group of best-adapted individuals in the chimp troop. The developed optimization procedure was adapted to optimization ***/valueThe proposed optimization algorithm and optimizing procedure can be successfully applied to solve the design problems of the LSPMSM.

关键词： Optimal design optimization chimp optimization algorithm Line-start permanent magnet synchronous motor Finite element method

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Dwarf Mongoose chimp optimization Enabled RMDL for Sentiment Categorization Using Cell Phone Data

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年第1期24卷 197-222页

作者： Abraham, Minu P. Reddy, K. R. Udaya Kumar Nitte Deemed Univ NMAM Inst Technol NMAMIT Dept Comp Sci & Engn Karkala 574110 Karnataka India Dayananda Sagar Univ Bengaluru Karnataka India

Sentiment analysis is the process of looking through digital text to determine if the emotional tone of a text is positive, negative, or neutral. It helps companies improve their product, but a serious problem arises in classifying the polarity of certain texts with information, sentences or features to forecast their opinion. Therefore, sentiment classification should be done using new technology that classifies reviews as positive or negative so that users can make effective decisions. This research paper develops an effective model to classify sentiment using cell phone data. Initially, the Amazon phone document is passed to the BERT tokenization stage to split the acquired reviews. Then, the Aspect Term Extraction (ATE) is applied and the Term Frequency-Inverse Document Frequency (TF-IDF) is extracted as the first output. Afterward, Wordnet ontology features are extricated as the second output. Moreover, features like statistical, sarcasm linguistic, and N-gram features are extracted from BERT tokenization and considered as the third output. Finally, the sentiment is classified by subjecting the obtained three outputs to Random Multimodal Deep Learning (RMDL), which is tuned by Dwarf Mongoose chimp optimization (DMCO). DMCO is created by the combination of the Dwarf Mongoose optimization (DMO) and the chimp optimization algorithm (ChOA). The developed DMCO-RMDL approach attained high accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, recall, and F1-score values of 93%, 92.8%, 92.2%, 91.5%, 94.1%, and 94.8%, respectively.

关键词： Aspect term extraction cell phone review data sentiment classification random multimodal deep learning dwarf mongoose chimp optimization dwarf mongoose optimization chimp optimization algorithm

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A Novel Technique for optimization of Artificial Neural Network Using Ensemble of chimp, Harris Hawks and Manta Ray Foraging optimization algorithms for Enhancing Software Maintainability Prediction

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-35页

作者： Goel, Varun Kaur, Arvinder Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol Sec 16C New Delhi 110078 India Guru Gobind Singh Indraprastha Univ Univ Sch Automat & Robot Surajmal Vihar New Delhi 110032 Delhi India Guru Gobind Singh Indraprastha Univ Univ Sch Design & Innovat Surajmal Vihar New Delhi 110032 India

For improving software maintainability prediction (SMP), an efficient method for SMP is required in early stages of SDLC to avoid higher maintenance costs involved with the software. To address this issue, we have used three metaheuristic algorithms, i.e., chimp optimization algorithm (ChOA), Harris hawks optimization (HHO) and manta ray foraging optimization (MRFO) algorithm. chimp optimization algorithm has two variants: ChOA1 and ChOA2 depending on the dynamic coefficients of h vector. These metaheuristic algorithms are used for artificial neural network (multilayer perceptron) hyperparameters optimization which can be useful for improving the predictive capability of ANN which will then correctly predict software maintainability (change) using various OO metrics. In this study, a novel HCHHMRFO algorithm has been developed to select the best hyperparameters of ANN (multilayer perceptron) to build the model, which can predict software maintainability accurately. Performance evaluation of models developed after hyperparameters tuning with ChOA1, ChOA2, HHO, MRFO, Ensemble of ChOA, HHO and MRFO (ChOA1_HHO_MRFO and ChOA2_HHO_MRFO) is performed using performance indicators including MAE and RMSE. UIMS and QUES datasets have been used to assess this work. Tent map and singer map are the best chaotic maps (in terms of iterations;RMSE;and MAE, respectively) that are used for ANN's hyperparameters initialization on UIMS dataset. Gauss/mouse map and tent map are the best chaotic maps (in terms of iterations;RMSE and MAE, respectively) that are used for ANN's hyperparameters initialization on the QUES dataset. Comparison results suggest that ChOA2_HHO_MRFO Ensemble is found to give better results to tune ANN hyperparameters for SMP for both the datasets, i.e., UIMS and QUES.

关键词： chimp optimization algorithm Harris hawks optimization Manta ray foraging optimization Artificial neural network Software maintainability Chaotic maps

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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 159-216页

作者： Liu, Qingxin Li, Ni Jia, Heming Qi, Qi Abualigah, Laith Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hainan Normal Univ Sch Math & Stat Haikou 571158 Peoples R China Hainan Normal Univ Key Lab Data Sci & Intelligence Educ Minist Educ Haikou 571158 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles' position are adjusted by the chimp optimization algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA's segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency.

关键词： Remora optimization algorithm chimp optimization algorithm Multi-level thresholding image segmentation Cross-entropy

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Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm

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SOLAR ENERGY 2023年第1期253卷 343-359页

作者： Yang, Chao Su, Chang Hu, Haiting Habibi, Mostafa Safarpour, Hamed Khadimallah, Mohamed Amine Guangdong Univ Technol Sch Adv Mfg Jieyang 522000 Guangdong Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 113659567 Tehran Iran Imam Khomeini Int Univ Fac Engn Dept Mech Qazvin Iran Prince Sattam Bin Abdulaziz Univ Coll Engn Civil Engn Dept Al Kharj 16273 Saudi Arabia Univ Carthage Polytech Sch Tunisia Lab Syst & Appl Mech Tunis Tunisia

The global shift toward solar energy has resulted in the advancement of research into the manufacture of high-performance solar cells. It is critical to accurately model and identify the parameters of solar cells. Numerous models of solar cells have been presented thus far, including the single-diode, the double-diode, and the three-diode models. Every model contains a number of unidentified parameters, and numerous approaches for determining their optimal values have been published in the literature. The purpose of this article is to propose an efficient optimization technique, dubbed the chimp optimization algorithm (ChOA), for estimating the model parameters of solar networks. The proposed ChOA outperforms state-of-the-art algorithms in terms of conver-gence rate, global search capacity, and durability. To demonstrate the proposed ChOA algorithm's efficiency, it is used to determine the parameters of several photovoltaic modules and solar cells. The result of ChOA is evaluated and compared with ten well-known optimization algorithms in the literature. Additionally, the performance of the ChOA algorithm has been evaluated in a practical application for parameter evaluation of three widely-utilized commercial modules, i.e., multi-crystalline (KC200GT), polycrystalline (SW255), and monocrystalline (SM55), under a variety of temperature and irradiance circumstances that result in alterations in the photovoltaic model's parameters. The results confirm the proposed algorithm's robustness and high performance.

关键词： optimization Solar cell ChOA Photo Voltaic Modules chimp optimization algorithm

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Synergistic Spectrum Sensing: MME and Energy Double Thresholding Enhanced by Hybrid Whale-chimp algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2025年第3期140卷 1057-1084页

作者： Kumar, A. J. Sharath Raghavendra, L. R. Manjunatha, R. C. Nethravathi, H. M. Vidyavardhaka Coll Engn Dept Elect Commun Engn Mysuru 570002 Karnataka India Global Acad Technol Dept ECE Bengaluru 560098 Karnataka India Adichunchanagiri Univ BGS Inst Technol Dept Elect & Commun Engn Mandya 571448 Karnataka India Marvell India Pvt Ltd Bengaluru India

Spectrum sensing is a critical function in cognitive radio networks, enabling the identification of available frequency bands without interfering with primary users. To improve the effectiveness of energy detection, we propose an adaptive double-threshold method that dynamically adjusts the upper and lower thresholds based on the signal-to-noise ratio (SNR) of cognitive nodes. This research introduces a novel framework for determining the optimal weighting coefficients necessary for these threshold adjustments. Specifically, we present the hybrid Whale-chimp optimization algorithm (WCOA), which ensures stable threshold adaptation, mitigates the sensitivity to minor coefficient fluctuations, and keeps thresholds within an optimal range. Furthermore, we integrate the adaptive double-threshold method with a hybrid detection approach combining Energy Detection and Maximum-Minimum Eigenvalue (MME), which is further fine-tuned using the proposed Innovative Hybrid Whale-chimp algorithm. Our approach effectively addresses the limitations of conventional energy detection methods, particularly under low SNR conditions. Collaborative interactions among cognitive nodes enhance detection accuracy, leading to faster spectrum sensing and improved detection probabilities. The proposed method offers a reliable solution for efficient spectrum sensing while safeguarding the integrity of primary users.

关键词： chimp optimization algorithm Cognitive radio Energy detection Maximum-minimum eigenvalue Energy double thresholding Signal-to-noise ratio Spectrum sensing Whale optimization Probability of misdetection Genetic algorithm Particle swarm optimization Genetic algorithm-particle swarm optimization Scale learning factor

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Vibration Modeling And Simulation Analysis Of Wind Turbine Flexible Tower Based On ChOA algorithm optimization

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第1期28卷 109-118页

作者： Yin, Xiao Ju Mu, Qi Zheng Zhou, Li Li, Bo Shao, Guo Ce Du, Zhi Liang Shenyang Inst Engn Dept Renewable Energy Shenyang 110136 Peoples R China CGN New Energy Investment Shenzhen Co Ltd Liaoning Branch Shenyang 110623 Peoples R China Shenyang Inst Engn Coll Informat Shenyang 110136 Peoples R China

The vibration of flexible towers of wind turbines can cause accidents or even the shutdown of wind turbines from time to time. Establishing a tower vibration prediction model can effectively predict the occurrence of accidents. Aiming at the problems of low prediction accuracy of the BP neural network and easy to fall into the local optimal solution, the BP neural network is optimized using the chimp optimization algorithm (ChOA). To confirm the algorithm's feasibility, the 120m flexible tower data of a 2 MW wind turbine in a wind farm is simulated and analyzed, and the tower vibration prediction model is used to establish by optimizing the heterogeneous data from multiple sources through correlation analysis under different operating conditions of the wind turbine to find out the correlation variables affecting the vibration of the flexible tower. The results show that the ChOABP neural network has the best prediction effect under the rated wind speed, the root mean square error (RMSE) decreases by 12.1267, and the mean absolute error (MAE) decreases by 9.688, and the error-index decreases by more than the rated wind speed, which proves that the algorithm is better than the optimized BP neural network in rated wind speed.

关键词： Wind turbine Tower chimp optimization algorithm BP neural network Predictive modeling

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