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Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching-learning-based optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第8期31卷 p1-24页

作者： Taheri, Seyed Iman Salles, Mauricio B. C. Khan, Irfan Ahmad Univ Sao Paulo Lab Adv Elect Grids LGrid Sao Paulo Brazil Texas A&M Univ Clean & Resilient Energy Syst CARES Lab College Stn TX 77802 USA

The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching-learning-based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi-opposition-based-learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability-value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

关键词： distributed energy resources distributed generation distribution network energy management heuristic algorithm optimization algorithm

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Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期45卷 10603-10617页

作者： Huang Jingjing Zhang Xu Hefei Univ Econ Sch Financial Management Hefei 230011 Peoples R China Zhengqi Holding Co Ltd Hefei Peoples R China

In view of the individual differences in learners' abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners;the project response theory is used to analyze the ability of different learners;According to the learners' learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions;Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm;The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax laware obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model;that is, the convergence performance and distribution performance of the model is better;This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.

关键词： Chaotic mapping particle swarm optimization algorithm offline courses of tax law resource intelligence recommendation

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Prairie Dog optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 20017-20065页

作者： Ezugwu, Absalom E. Agushaka, Jeffrey O. Abualigah, Laith Mirjalili, Seyedali Gandomi, Amir H. Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Rd ZA-3201 Pietermaritzburg Kwazulu Natal South Africa Fed Univ Lafia Dept Comp Sci Lafia 950101 Lafianasarawa S Nigeria Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

This study proposes a new nature-inspired metaheuristic that mimics the behaviour of the prairie dogs in their natural habitat called the prairie dog optimization (PDO). The proposed algorithm uses four prairie dog activities to achieve the two common optimization phases, exploration and exploitation. The prairie dogs' foraging and burrow build activities are used to provide exploratory behaviour for PDO. The prairie dogs build their burrows around an abundant food source. As the food source gets depleted, they search for a new food source and build new burrows around it, exploring the whole colony or problem space to discover new food sources or solutions. The specific response of the prairie dogs to two unique communication or alert sound is used to accomplish exploitation. The prairie dogs have signals or sounds for different scenarios ranging from predator threats to food availability. Their communication skills play a significant role in satisfying the prairie dogs' nutritional needs and anti-predation abilities. These two specific behaviours result in the prairie dogs converging to a specific location or a promising location in the case of PDO implementation, where further search (exploitation) is carried out to find better or near-optimal solutions. The performance of PDO in carrying out optimization is tested on a set of twenty-two classical benchmark functions and ten CEC 2020 test functions. The experimental results demonstrate that PDO benefits from a good balance of exploration and exploitation. Compared with the results of other well-known population-based metaheuristic algorithms available in the literature, the PDO shows stronger performance and higher capabilities than the other algorithms. Furthermore, twelve benchmark engineering design problems are used to test the performance of PDO, and the results indicate that the proposed PDO is effective in estimating optimal solutions for real-world optimization problems with unknown global optima. The PDO

关键词： Nature-inspired Prairie dogs optimization algorithm Anti-predation Burrow-building optimization Swarm intelligence

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

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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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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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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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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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