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Strengthened teaching-learning-based optimization algorithm for numerical optimization tasks

EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1463-1480页

作者： Chen, Xuefen Ye, Chunming Zhang, Yang Zhao, Lingwei Guo, Jing Ma, Kun Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching-learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO's exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1-f7) and six multimodal tasks (f8-f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).

关键词： Metaheuristic optimization algorithm Teaching-learning-based optimization algorithm Teaching factor Elite system Cauchy mutation

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A deep neural network with hybrid spotted hyena optimizer and grasshopper optimization algorithm for copy move forgery detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 24547-24572页

作者： Gupta, Ruchi Singh, Pushpa Alam, Tanweer Agarwal, Shivani Abdul Kalam Tech Univ Ajay Kumar Garg Engn Coll Dept Informat Technol Lucknow Uttar Pradesh India Abdul Kalam Tech Univ GL Bajaj Inst Technol & Management Dept Comp Sci Lucknow Uttar Pradesh India Islamic Univ Madinah Dept Comp & Informat Syst Madinah Saudi Arabia Chandigarh Univ Apex Inst Technol Dept Comp Sci Chandigarh Punjab India

Protecting data against tampering is a significant concern in modern times. Digital photographs are essential for displaying information. Digital picture forgeries include adding unusual patterns to real pictures, causing visual heterogeneity. CMF is a sort of digital image forgery in which a segment of an image is linked to a similar picture to cover or recreate forgeried components. The forgery seems authentic since the goal area has the same qualities as the original. Despite several ways to detect CMFD, there exist research gaps such as false detection, excessive execution time and low accuracy. Therefore, to address this issue, we present a hybrid optimization technique and classifier for CMFD. Proposed a novel deep learning technique stacked sparse denoising autoencoder (SSDAE) to classify the images as legitimate or fake. Additionally, the weight and bias parameters of the SSDAE model are optimized using the Grasshopper optimization algorithm (GOA) and the Spotted Hyena optimizer (SHO). The experiments are conducted on MICC-F220, MICC-F600, MICC-F2000 and CASIA2.0 datasets. Experimental results indicate that the proposed scheme find out image forgery region with Accuracy = 97.45%;Precision = 98.75%;Recall = 98.25% and F1 = 98.55% on MICC-F200 dataset, Accuracy = 98.92%;Precision = 88.45%;Recall = 85.21% and F1 = 91.41% on MICC-F600 dataset, Accuracy = 99.12%;Precision = 99.25%;Recall = 91.14% and F1 = 85.32% on MICC-F2000 dataset and Accuracy = 98.02%;Precision = 96.03%;Recall = 97.74% and F1 = 97.48% on CASIA 2.0 dataset.

关键词： Image forgery Copy move forgery Deep learning optimization algorithm Tampered images Image authentication

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optimization algorithm for rapid 3D gravity inversion

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Applied Geophysics 2019年第4期16卷 507-518,561页

作者： Jing Lei Yao Chang-Li Yang Ya-Bin Xu Meng-Long Zhang Guang-Zhi Ji Ruo-Ye School of geophysics and information technology China University of Geosciences(Beijing)Beijing 100083China The National Center for Geological Exploration Technology Langfang 065000China Institute of Geophysical and Geochemical Exploration Chinese Academy of Geological SciencesLangfang 065000China.

The practical application of 3D inversion of gravity data requires a lot of computation time and storage *** solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity *** verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.

关键词： Gravity 3D inversion optimization algorithm high efficiency massive data

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Reliability Prediction of the Distribution Network Based on Wavelet Neural Network with Quantum Particle Swarm optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第4期51卷 398-408页

作者： Ling, Chengxiang Li, Tianyu Lu, Mengke Zhou, Xiaobo Wu, Ye Su, Yijing Gao, Xinyue Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China Harbin Inst Technol Sch Architecture Shenzhen Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China China Energy Engn Grp Zhejiang Elect Power Design Inst Co Ltd Hangzhou Peoples R China

For distribution networks with fuzzy network structure or large scale, traditional reliability assessment is limited by data collection and lack of data samples. Compared with traditional methods, the distribution network reliability prediction method can use fewer data to calculate and obtain reliability results, and its operation is simpler and more practical. In this paper, a distribution network original parameters and reliability prediction method based on wavelet neural network (WNN) and quantum particle swarm optimization algorithm (QPSO) is proposed. Firstly, given the blindness of mother wavelet selection, this paper analyses the error and running time through example analysis and selects the most suitable mother wavelet for distribution network reliability prediction. According to the characteristics of premature convergence of QPSO, the evolutionary speed factor and aggregation factor are introduced to modify the scaling factor to control the convergence of the algorithm. The improved QPSO is used to optimize the initial values and thresholds of the WNN. It can reduce their influence on the prediction results. Finally, the analysis results of different examples show that the method has higher forecast accuracy, better generalization ability, and stability. This method also provides new scientific ideas for the reliability prediction of distribution networks.

关键词： index terms-distribution network reliability wavelet neural network quantum particle swarm optimization algorithm

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Ensemble recurrent neural network with whale optimization algorithm-based DNA sequence classification for medical applications

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SOFT COMPUTING 2023年 1-14页

作者： Alshammari, Abdulaziz Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Informat Syst Dept Riyadh Saudi Arabia

The modern data-driven era has facilitated the gathering of large quantities of biomedical and clinical data. The deoxyribonucleic acid gene expression datasets have become a vital focus for the research community because of their capability to detect pathogens via 'biomarkers' or particular modifications in the gene sequence which portray a specific pathogen. Metaheuristic-related feature selection (FS) efficiently filters out only the pertinent genes out of large feature sets to lessen the data storage and computation requirements. This paper embraces the whale optimization algorithm for the FS issue in HD microarray data for the effectual propagation of candidate solutions to reach global optima over sufficient iterations. The chosen data are classified by employing an ensemble recurrent network (ERNN) that retains the amalgamation of long short-term memory, bidirectional long short-term memory, and gated recurrent units. Analysis of this proposed ERNN methodology would be performed by correlating with diverse advanced methodologies, and thus, the ERNN attains 99.59% precision and 99.59% accuracy.

关键词： DNA Gene Preprocessing Feature selection optimization algorithm Recurrent neural network

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Research on identification of irrational fractional-order systems in frequency domain based on optimization algorithm

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2019年第15期41卷 4351-4357页

作者： Chen Lanfeng Xue Dingyu Northeastern Univ Sch Informat Sci & Engn Shenyang Liaoning Peoples R China Shenyang Normal Univ Coll Phys Sci & Technol Shenyang Liaoning Peoples R China

Fractional-order calculus can obtain better results than the integer-order in control theory, so it has become a research hotspot in recent years. However, the structure of the irrational fractional-order system is complex, so its theoretical analysis and controller design are more difficult. In this paper, a method based on convolution integral is proposed to obtain the frequency domain response of the irrational model. Combined with the optimization algorithm, the model parameters are identified. Moreover, the rationalization of the irrational model is realized, which facilitates the analysis and application design of this kind models. Finally, two examples are given to illustrate the effectiveness and feasibility of the method by identifying parameters and rationalization.

关键词： Irrational fractional-order system convolution integral optimization algorithm model parameters rationalization of the irrational model

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A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach

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SOFT COMPUTING 2019年第6期23卷 1901-1916页

作者： Selvi, C. Sivasankar, E. Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India

Recommender system (RS) is an emerging technique in information retrieval to handle a large amount of online data effectively. It provides recommendation to the online user in order to achieve their correct decisions on items/services quickly and easily. Collaborative filtering (CF) is one of the key approaches for RS that generates recommendation to the online user based on the rating similarity with other users. Unsupervised clustering is a class of model-based CF, which is more preferable because it provides the simple and effective recommendation. This class of CF suffers by higher error rate and takes more iterations for convergence. This study proposes a modified fuzzy c-means clustering approach to eliminate these issues. A novel modified cuckoo search (MCS) algorithm is proposed to optimize the data points in each cluster that provides an effective recommendation. The performance of proposed RS is measured by conducting experimental analysis on benchmark MovieLens dataset. To show the effectiveness of proposed MCS algorithm, the results are compared with popular optimization algorithms, namely particle swarm optimization and cuckoo search, using benchmark optimization functions.

关键词： Recommender system (RS) Collaborative filtering (CF) Clustering approach optimization algorithm Modified fuzzy c-means (MFCM) Modified cuckoo search (MCS)

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optimization algorithm of Massive Data Query Based on JOIN

Optimization Algorithm of Massive Data Query Based on JOIN

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5th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zheng Jiajia Sun Jiasong Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781479932795

The paper uses parallel computation of grid and data grid as theoretical basis. The isomerism of each node and difference of communication rate under grid environment, and massive data query makes query operations of database difficult. For the new structural characteristic, the paper proposes a parallel JOIN algorithm based on massive data, it fully uses parallelism and reduces transmission volume to shorten response time. The paper includes implementation and optimization of JOIN algorithm, fault-tolerant mechanism, RDMM, partition and transmission of massive data.

关键词： component grid computation parallel computing massive data optimization algorithm

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Parameter Identification of Single-Phase Inverter Based on Improved Moth Flame optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第4-5期47卷 456-469页

作者： Wu, Zhongqiang Shen, Dandan Shang, Mengyao Qi, Songqi Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. Moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.

关键词： parameter identification fault diagnosis optimization algorithm single-phase inverter transfer function model moth flame optimization algorithm particle swarm optimization genetic algorithm Fast Fourier transform Levy flight

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Study on modeling of multispectral emissivity and optimization algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2006年第1期17卷 238-242页

作者： Yang, CL Yu, Y Zhao, DY Zhao, GL Harbin Inst Technol Dept Elect Engn Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Dept Automat Harbin 150001 Heilongjiang Peoples R China

Target's spectral emissivity changes variously, and how to obtain target's continuous spectral emissivity is a difficult problem to be well solved nowadays. In this letter, an activation-function-tunable neural network is established, and a multistep searching method which can be used to train the model is proposed. The proposed method can effectively calculate the object's continuous spectral emissivity from the multispectral radiation information. It is a universal method, which can be used to realize on-line emissivity demarcation.

关键词： neural networks optimization algorithm spectral emissivity tunable activation function

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