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Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems

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APPLIED INTELLIGENCE 2023年第6期53卷 6763-6799页

作者： He, Jieguang Peng, Zhiping Cui, Delong Qiu, Jingbo Li, Qirui Zhang, Hao Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault Maoming Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

The sooty tern optimization algorithm (STOA) is a newly proposed bio-inspired algorithm that mimics the migration and attacking behaviors of the sea bird sooty tern in nature. STOA has several excellent advantages, including fewer parameters, a simple structure, a fast convergence rate, and high exploitation. Nevertheless, it is difficult to find the global optimal solution and prone to losing population diversity when dealing with complex optimization problems due to its single search guidance strategy and position update method. An enhanced STOA (ESTOA) is proposed to address these shortcomings that incorporates multiple search guidance strategies and position update modes. In terms of search guidance, in addition to the best individual in the original STOA, the mean individual and a randomly selected individual are also designed to guide the search. Six position update modes are proposed in conjunction with the guidance strategies, including one improved scaling mode with an extended spiral radius and five other modes based on offset operations. Due to their distinct design objectives, these guidance strategies and position update modes exhibit varying levels of search intensity and optimization effect. However, they complement one another and work cooperatively to achieve a good balance of global exploration and local exploitation. Several widely used sets of benchmark functions with a wide range of dimensions and varying degrees of complexity are used to validate ESTOA's performance. The obtained results are compared to those of other state-of-the-art optimization algorithms in terms of convergence accuracy and a variety of numerical performance evaluation parameters. A significant improvement in solution quality demonstrates that ESTOA can increase population diversity and maintain a good balance between global exploring and local exploiting abilities.

关键词： sooty tern optimization algorithm Bio-inspired algorithm optimization Search guidance Position update

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optimization of Airflow Distribution in Mine Ventilation Networks Using the Modified sooty tern optimization algorithm

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MINING METALLURGY & EXPLORATION 2024年第1期41卷 239-257页

作者： Wang, Jinmiao Xiao, Jun Xue, Yan Wen, Lixue Shi, Dongping Xiangtan Univ Sch Environm & Resources Xiangtan 411105 Peoples R China Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China

Mine ventilation energy consumption is one of the main sources of energy consumption in mining production, accounting for about one-third to one-half of the total energy consumption. Therefore, reducing the energy consumption of the mine ventilation system is crucial for mining production. With the premise of meeting the airflow requirements of the underground work areas and achieving sustainable development of mining production, this paper establishes a nonlinear optimization mathematical model for the mine ventilation network with the objective of minimizing the total power. Regarding the model, optimization of the decision variables was carried out using the method of minimum spanning tree. The constraints for air flow balance, wind pressure balance, and fan operating conditions were optimized using the exterior penalty function method, thereby transforming the model into a nonlinear unconstrained model. Based on this model, a modified sooty tern optimization algorithm (mSTOA) was proposed to achieve optimization. Improved the sooty tern optimization algorithm (STOA) by using the uniform reverse strategy, fitness value-distance balance selection strategy, and mutation strategy. The STOA, mSTOA, and three other classical optimization algorithms were applied to the optimization of a ventilation system of an actual mine. The experimental results show that after using mSTOA to optimize the ventilation network airflow distribution in the mine, the total energy consumption can be reduced by about 35.06% while meeting the constraints and demands of the ventilation network regulation and usage. This is of great value to mine roadway operation environment safety and clean production.

关键词： Ventilation network optimization Sustainable development sooty tern optimization algorithm Reverse learning Fitness-distance balance selection Mutation operation

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sooty tern optimization algorithm for Solving the Multi-Objective Dynamic Economic Emission Dispatch Problem

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INternATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第1期13卷

作者： Soni, Jatin Bhattacharjee, Kuntal Nirma Univ Inst Technol Ahmadabad Gujarat India

The sooty tern optimization algorithm (STOA) has been used in this study to solve and optimize the dynamic economic emission dispatch (DEED) problem. The main aim of the DEED model is to minimize total fuel cost and emission of pollutant gases from thermal generators for 24 hours. The various operating constraints like valve point loading effect, ramp rate limit, transmission losses, operating conditions, and power balance constraints have been considered in this study to get a closer practical system. The swarm intelligence-based STOA method has been inspired by the migration and attacking behaviors of sea bird sooty tern. The exploration and exploitation approach of the proposed algorithm help to get an optimum solution in less convergence time. The algorithm has been tested in 5 and 10 thermal generating units to verify the algorithm's performance. The results obtained by the proposed algorithm have been compared with results obtained by other recently developed algorithms.

关键词： Dynamic Economic Emission Dispatch Ramp-Rate Limit sooty tern optimization algorithm Transmission Loss Valve Point Loading Effect

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Application of sooty tern optimization algorithm on Solar-Wind-Battery-Thermal Integrated Dynamic Economic Emission Dispatch Problems

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第0期

作者： Ghorui, Sudip Kumar Dasgupta, Koustav Ghoshal, Sakti Prasad Budge Budge Inst Technol Dept Elect Engn Kolkata West Bengal India Techno India Univ Dept Elect Engn Salt Lake West Bengal India

The current challenges faced by conventional power plants, including increasing load demand over time, high generation costs, and excessive emissions from fossil fuels, have been helped to overcome by the integration of renewable energy sources (RESs) alongside conventional thermal power units. In this article, traditional dynamic economic emission dispatch (DEED) is enhanced by incorporating RESs, including two solar units, two wind units, and one battery power unit, forming the solar-wind-battery-thermal (SWBT) integrated DEED (SWBTDEED). This integration aimed at reducing generation costs and minimizing excessive fuel emissions, and combining cost and emissions over a 24 h period. Four different test systems, each incorporating 6, 10, 30, and 40 thermal units alongside the same RESs, are considered to meet varying load demands hourly throughout the day. This article demonstrates that, in addition to reducing generation costs, SWBTDEED is capable of reducing emissions by 38.28%, 28.48%, 20.67%, and 20.44% for four test systems, thereby protecting the environment. The sooty tern optimization algorithm (STOA) is proposed in this article for solving complex DEED and SWBTDEED problems, considering various constraints such as generation limits, ramp rate limits, valve point loading, and Weibull distribution. Finally, the robustness, optimization efficiency, and capability of the STOA technique in handling complex nonlinear constraints are demonstrated in the results section, showcasing its ability to achieve optimal results compared to other algorithms such as the sine-cosine algorithm, backtracking search algorithm, differential evolution, and particle swarm optimization.

关键词： battery power unit dynamic economic emission dispatch solar energy sooty tern optimization algorithm thermal energy wind energy

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A Multi-Strategy Improved sooty tern optimization algorithm for Concrete Dam Parameter Inversion

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WATER 2024年第1期16卷 119页

作者： Ma, Lin Ma, Fuheng Cao, Wenhan Lou, Benxing Luo, Xiang Li, Qiang Hao, Xiaoniao Nanjing Hydraul Res Inst Nanjing 210029 Peoples R China Yangling Vocat & Tech Coll Xianyang 712100 Peoples R China Minist Water Resources Water Gate Safety Management Ctr Nanjing 210029 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Xinchang Cty Water Resources & Hydropower Bur Shaoxing 312500 Peoples R China

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved sooty tern optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis.

关键词： multi-strategy improvement sooty tern optimization algorithm displacement statistical model concrete parameter inversion concrete dams

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sooty-LuCaNet: sooty tern optimization based deep learning network for lung cancer detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8823-8836页

作者： Muthazhagan, B. Ravi, T. Rajinigirinath, D. Kings Engn Coll Dept Comp Sci Engn Chennai Tamil Nadu India Shadan Womens Coll Engn & Technol Dept Comp Sci Engn Hyderabad Telangana India Sri Muthukumaran Inst Technol Dept Comp Sci Engn Chennai Tamil Nadu India

Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer.

关键词： Lung cancer computed tomography deep learning Shufflenet sooty tern optimization algorithm

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Hybrid sooty tern optimization and Differential Evolution for Feature Selection

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Computer Systems Science & Engineering 2021年第12期39卷 321-335页

作者： Heming Jia Yao Li Kangjian Sun Ning Cao Helen Min Zhou College of Information Engineering Sanming UniversitySanming365004China College of Mechanical and Electrical Engineering Northeast Forestry UniversityHarbin150040China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets *** selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of *** recent years,it has been applied in many practical domains of intelligent *** application of SVM in many fields has proved its effectiveness in classification tasks of various *** performance is mainly determined by the kernel type and its *** of the most challenging process in machine learning is feature selection,intending to select effective and representative *** main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow ***,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed *** superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.

关键词： sooty tern optimization algorithm hybrid optimization feature selection support vector machine

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Hybrid and individual least square support vector regression methods for estimating the optimal moisture content of stabilized soil

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 2757-2771页

作者： Zhang, Junfang Du, Peng Hengshui Coll Sch Math & Comp Sci Hengshui 053000 Hebei Peoples R China Hengshui Coll Sch Elect & Informat Engn Hengshui 053000 Hebei Peoples R China

The Optimum moisture content (OMC)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(OMC)$$\end{document} is a pivotal factor in the composition of embankment fill soil materials employed in transportation construction. This article introduces a groundbreaking methodology for the anticipation of OMC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$OMC$$\end{document} in soil-stabilizer blends through the utilization of one of the Machine Learning (ML)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(ML)$$\end{document} models, including Least Square Support Vector Regression (LSSVR)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(LSSVR)$$\end{document} analysis. Furthermore, this study pioneers the creation of dedicated LSSVR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LSSVR$$\end{document} prediction models meticulously tailored to ensure the utmost precision in OMC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \

关键词： Optimum moisture content Least square support vector regression Beluga whale optimization sooty tern optimization algorithm

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Modeling the emitted carbon dioxide and monoxide gases in the gasification process using optimized hybrid machine learning models

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CHEMICAL PRODUCT AND PROCESS MODELING 2024年第6期19卷 1029-1051页

作者： Shen, Baohua Lu, Xiaoman Jiang, Juan Li, Daoguo Univ Warwick Dept Comp Sci Coventry CV4 7EZ England Hangzhou Dianzi Univ Informat Engn Coll Hangzhou 311035 Zhejiang Peoples R China

The machine learning methods are hereby proposed to predict the amount of Carbon Monoxide (CO) and Carbon Dioxide (CO2) emissions in a gasification process, which is one of the most important enabling technologies for carbon-containing materials, such as coal, biomass, and waste toward producing end products of worth, such as syngas, hydrogen, and synthetic fuels. In an attempt to support efforts for improving the emission prediction-a key criterion for enhancing efficiency and further, the environmental performance of gasification-two new advanced algorithms are being applied for the optimization of the model of a random forest: the Jellyfish Search Optimizer (JSO) and sooty tern optimization algorithm (STOA). The tuned RFJS (RF+JSO) was the best of these configurations, providing the least RMSE of 0.593 on test data and the highest R2 on validation of 0.983, proving to be most effective for the prediction of emissions. This goes to attest that the model RFJS would be a strong tool in real-time-based carbon emissions reduction due to its effectiveness in dealing with major implications from environmental monitoring to regulation and further into sustainable energy production.

关键词： gasification process machine learning techniques random forest Jellyfish Search optimizer sooty tern optimization algorithm

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Prediction Of Compressive Strength Of High-performance Concrete Using Multi-layer Perceptron

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2024年第7期27卷 2719-2733页

作者： Wu, Xu Yan, Guifeng Zhang, Wei Bao, Yuping Nantong Inst Technol Dept BIM Res Nantong 226002 Jiangsu Peoples R China

The correlations between the mechanical properties of HPCs and their mixture compositions are complex, non-linear, and complex to characterize employing standard statistical methods. This paper aimed to estimate HPC's compressive strength using a machine learning algorithm including Multi-layer Perceptron (MLP) with an HPC mixed collection of 168 samples via eight input variables. In addition, three meta-heuristic optimizers have been used for improving the efficiency and accuracy of MLP, which are included Dandelion optimization (DO), Aquila Optimizer (AO), and sooty tern optimization algorithm (STOA). After fitting the presented models, the developed models' predictive generalization and efficiency ability is evaluated against a set of performance parameters. All models used were found to perform as suitable in predicting outcomes, which can be employed for saving time and energy. As a result, Aquila's optimization had the most accurate by MLP compared to other hybrid models. MLAO3 obtained R-2 = 0.994 and RMSE = 1.27(MPa), which are the most suitable result compared to other models.

关键词： High-performance concrete Compressive strength Multi-Layer Perceptron Dandelion optimization Aquila Optimizer sooty tern optimization algorithm

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