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Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using chimp optimization and Deep Belief Neural Network

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OPTICAL MEMORY AND NEURAL NETWORKS 2023年第4期32卷 275-294页

作者： Prasad, Shailandra Kumar Sinha, Mrityunjay Kumar Natl Inst Technol Jamshedpur 831001 Jharkhand India

In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using chimp optimization algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 U, and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.

关键词： U-tube heat exchanger unilateral ladder-type helical baffles pressure drop chimp optimization algorithm intelligent prediction system

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Dynamic Levy Flight chimp optimization

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KNOWLEDGE-BASED SYSTEMS 2022年 235卷 1页

作者： Kaidi, Wei Khishe, Mohammad Mohammadi, Mokhtar Invest & Technol Dev Co Ltd Zhengzhou Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Regio Iraq

Background: The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents' insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. Objective: This paper introduces a Dynamic Levy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase. Methods: To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Levy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019). Result and conclusion: Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： optimization chimp optimization algorithm Swarm-intelligence Levy Flight Dynamic search

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Nodes clustering and multi-hop routing protocol optimization using hybrid chimp optimization and hunger games search algorithms for sustainable energy efficient underwater wireless sensor networks

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年 35卷

作者： Yang, Yang Wu, Yunqiang Yuan, Hongji Khishe, Mohammad Mohammadi, Mokhtar Nanchang Inst Technol Sch Elect & Informat Nanchang 330044 Jiangxi Peoples R China Nanchang Inst Technol Coll Comp Informat & Engn Nanchang 330044 Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Iraq

Clustering and routing processes in underwater wireless sensor networks (UWSNs) are challenging tasks in the underwater environment due to the multiplicity of sensor nodes, transmission bandwidth, and limited energy resources. In order to address the shortcomings mentioned above, this paper proposes a novel hybrid chimp optimization and Hunger Games Search (ChOA-HGS) algorithms for clustering and multi-hop routing optimization in UWSNs. In this approach, first, the ChOA is used to choose cluster heads and efficiently structure clusters. Then, the HGS-based routing procedure is used to determine the network's best pathways. The proposed approach combines the advantages of clustering and routing, resulting in optimal network lifetime and energy efficiency. The proposed ChOA-HGS is validated using a variety of measures after it is simulated using three different scenarios. In order to evaluate the performance of the ChOA-HGS, results are compared to PSO, MPSO, IPSO-GWO, TEEN, and LEACH. The results show that the ChOA-HGS outperformed other benchmarks in terms of lifetime and energy consumption.

关键词： Clustering chimp optimization algorithm Routing Hierarchical Energy efficiency

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optimization of constraint engineering problems using robust universal learning chimp optimization

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ADVANCED ENGINEERING INFORMATICS 2022年 53卷

作者： Liu, Lingxia Khishe, Mohammad Mohammadi, Mokhtar Mohammed, Adil Hussein Weifang Univ Dept Math Weifang 261061 Sandong Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Regio Iraq Cihan Univ Erbil Fac Engn Dept Commun & Comp Engn Erbil Kurdistan Regio Iraq

Due to the challenging constraint search space of real-world engineering problems, a variation of the chimp optimization algorithm (ChOA) called the Universal Learning chimp optimization algorithm (ULChOA) is proposed in this paper, in which a unique learning method is applied to all previous best knowledge obtained by chimps (candid solutions) to update prey's positions (best solution). This technique preserves the chimp's variety, discouraging early convergence in multimodal optimization problems. Furthermore, ULChOA introduces a unique constraint management approach for dealing with the constraints in real-world constrained optimization issues. A total of fifteen commonly recognized multimodal functions, twelve real-world constrained optimization challenges, and ten IEEE CEC06-2019 suit tests are utilized to assess the ULChOA's performance. The results suggest that the ULChOA surpasses sixteen out of eighteen algorithms by an average Friedman rank of better than 78 percent for all 25 numerical functions and 12 engineering problems while outperforming jDE100 and DISHchainle + 12 by 21% and 39%, respectively. According to Bonferroni-Dunn and Holm's tests, ULChOA is statistically superior to benchmark algorithms regarding test functions and engineering challenges. We believe that the ULChOA proposed here may be utilized to solve challenges requiring multimodal search spaces. Furthermore, ULChOA is more widely applicable to engineering applications than competitor benchmark algorithms.

关键词： Universal learning chimp optimization algorithm Real-parameter optimisation Multimodal functions

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Leveraging quantum-inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems

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EXPERT SYSTEMS 2024年第8期41卷 e13563-e13563页

作者： Zhang, Lin Alsubai, Shtwai Alqahtani, Abdullah Alanazi, Abed Abualigah, Laith Wuhu Inst Technol Coll Network Engn Wuhu Anhui Peoples R China Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci Al Kharj Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Software Engn Dept Al Kharj Saudi Arabia Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Middle East Univ MEU Res Unit Amman Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Isra Univ Fac Informat Technol Amman Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya Malaysia Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Yuan Ze Univ Coll Engn Taoyuan Taiwan

Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum-behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN-based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best-modified versions of CHOA, the quantum-behaved whale optimization algorithm (QWOA), and the quantum-behaved grey wolf optimizer (QGWO) as the two best quantum-behaved optimizers as well as classic CHOA. The most effective deep learning-based predictors for forecasting the profit, ranked from highest to lowest, are DNN-QCHOA, DNN-NCHOA, DNN-QWOA, DNN-QGWO, DNN-ULCHOA, DNN-CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN-CHOA is shown to be the most accurate model.

关键词： chimp optimization algorithm deep learning financial profit prediction quantum-behaved

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A novel fuzzy hybrid red fox chimp for optimal power flow in FACTS devices

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ELECTRICAL ENGINEERING 2023年第6期105卷 4481页

作者： Mahadevan, J. Rengaraj, R. Anna Univ Fac Elect Engn Chennai India Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Elect Engn Chennai India

Due to the large-scale, multimodal, non-convex, and nonlinear characteristics of OPF, mitigation of OPF problems in power transmission systems becomes a more complicated task. Though there already exist numerous optimization approaches to manage OPF issues, they remained unconstrained (i.e., fall into local optima issues). Therefore, in this paper, we proposed a novel fuzzy hybrid red fox chimp (FHRC) algorithm for handling a high level of uncertainties that occurs in power systems on equipping FACTS devices and renewable energy sources. Achieving optimal system variables using the proposed FHRC algorithm optimizes the chosen objective function rather than fulfilling operational inequality constraints and power flow equations. The integrated functioning of the red fox algorithm and chimp optimization algorithm with the fuzzy logic system efficiently optimizes the OPF problems. The efficiency of the proposed FHRC algorithm is evaluated using the IEEE 30 bus system in terms of measures, namely power generation cost, power loss, cost function value, and voltage deviations, under the application of diverse load conditions. The experimentation results reveal that the FHRC algorithm optimizes the objective function of OPF problems more than other compared methods. In future, our proposed approach will be implemented in various other energy storage systems and real power network.

关键词： Optimal power flow FACTS devices Red fox algorithm chimp optimization algorithm Fuzzy logic system Power loss Voltage deviation Power generation cost

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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Computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based chimp optimization algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedchimp optimization algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly algorithm(CNN-FA),Emperor Penguin optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure

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ISA TRANSACTIONS 2024年 147卷 304-327页

作者： Chen, Rongxing Xiao, Xinping Gao, Mingyun Ding, Qi Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Cent China Normal Univ Sch Informat Management Wuhan 430079 Peoples R China Nanjing Univ Sch Business Nanjing 210008 Peoples R China

The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure o

关键词： Mixed frequency sampling grey model Mixed data sampling chimp optimization algorithm Hard disk drive failure forecasting

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Deep learning-based hybrid sentiment analysis with feature selection using optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第28期82卷 43273-43296页

作者： Daniel, D. Anand Joseph Meena, M. Janaki VIT Chennai Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

In the past few years, sentiment analysis (SA) of online content has gained more attention in the research area due to the enormous increase of online content from various sources like websites, social blogs, etc. Many organizations use SA techniques to determine the opinion of users and to ensure their satisfaction. Numerous techniques are suggested by many researchers to identify the sentiments of online content. Among them, hybrid of deep learning and lexicon-based SA techniques are gaining more attention due to their outstanding performance than other approaches. Though the lexicon-based SA approaches integrated with deep learning SA approaches possess more advantages they suffer from lack of accuracy and scalability issues due to the high-dimensional features. To eliminate this issue, a hybrid SA approach is proposed in this paper with a bio-inspired feature selection technique. The Valence Aware Dictionary for Sentiment Reasoning (VADER) approach is integrated with the hybrid deep learning approach of attention-based bidirectional long short-term memory and variable pooling convolutional neural network (VPCNN-ABiLSTM) for SA. The optimal features are selected to minimize the scalability issue by integrating the chimp optimization algorithm with the opposition-based learning technique. The performance of the proposed approach is evaluated for four types of benchmark datasets in terms of precision, accuracy, recall, and F1 score. The proposed approach with OBL-CHOA based feature selection technique achieved higher accuracy of 97.1% with the reduction of 13.6% features. The accuracy of the proposed approach with the feature selection technique is 6.9% higher than the existing BiLSTM-CNN based SA approach.

关键词： Sentiment analysis VADER Convolutional neural network Deep learning Long short-term memory chimp optimization algorithm

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Micro-Calcification Classification Analysis in Mammogram Images with Aid of Hybrid Technique Analysis

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期128卷 1287-1307页

作者： Shanker, M. C. Vadivel, M. Sathyabama Inst Sci & Technol Dept Elect Engn Chennai Tamil Nadu India Vidya Jyothi Inst Technol Dept Elect & Commun Engn Hydrabad Andra Pradesh India

Breast cancer is the leading cause of death in women. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and chimp optimization algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).

关键词： Breast cancer classification Deep belief neural network Pre-processing Feature extraction Deep learning method chimp optimization algorithm

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