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Multilevel threshold segmentation of rice plant images utilizing tuna swarm optimization algorithm incorporating quadratic interpolation and elite swarm genetic operators

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Wang, Wentao Ye, Chen Pan, Zhongjie Tian, Jun Nankai Univ Coll Software Tianjin 300350 Peoples R China Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Peoples R China

Rice plant images exhibit varying texture characteristics across different growth stages and environmental conditions. A suitable image thresholding segmentation method can effectively separate the different feature regions of rice plants for better monitoring of rice growth to improve yield. This paper employs the Sigmoid nonlinear weights strategy, Quadratic interpolation strategy, and elite swarm Genetic strategy to enhance distinct stages of the tuna swarm optimization algorithm (TSO) to propose the SQGTSO algorithm, which has better convergence and global optimization capability. 10 CEC2017 benchmark functions are selected to validate the performance of the SQGTSO algorithm, and the experimental results show that the SQGTSO algorithm outperforms the other algorithms in 9 benchmark functions. To assess the feasibility and efficacy of the SQGTSO for multilevel threshold segmentation of rice plant images, this paper selects 8 rice plant images with diverse styles for the design of two sets of comparative experiments. The SQGTSO algorithm is comprehensively benchmarked against seven advanced metaheuristic algorithms and one machine learning method. Under the conditions of threshold levels ranging from 4 to 30, two distinct experiment sets are devised. In each set, Otsu's method and the MCET method are employed as fitness functions for the metaheuristic algorithms, respectively. The assessment criteria include fitness values, PSNR, SSIM, FSIM and HPSI. Additionally, the Friedman method is utilized for statistical analysis of the five metrics yielded by each algorithm. The experimental findings demonstrate the significant advantages of the SQGTSO method concerning five evaluation metrics and its convergence performance compared to other competitors.

关键词： Rice plant image Image threshold segmentation Metaheuristic algorithm tuna swarm optimization algorithm Elite swarm group strategy

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ReliefF guided variable spiral tuna swarm optimization algorithm with somersault foraging for feature selection

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ALEXANDRIA ENGINEERING JOURNAL 2025年 119卷 168-188页

作者： Wang, Yu-Cai Wang, Jie-Sheng Zhang, Min Song, Hao-Ming Hou, Jia-Ning Qi, Yu-Liang Song, Yu-Wei Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

The feature selection (FS) technique is a powerful knowledge discovery tool for understanding complex problems by identifying the most relevant features. With the rapid development of high-throughput technologies, highdimensional, multi-text and multi-classification data have become increasingly common, FS is considered as an effective method for dimension reduction. So a ReliefF guided variable spiral tuna swarm optimization (TSO) algorithm with somersault foraging was proposed to solve the FS problem. Firstly, inspired by the sudden flipping behavior of manta rays when capturing plankton, a novel somersault foraging strategy is introduced to help the TSO algorithm escape from local optima. Secondly, a ReliefF-guided strategy is incorporated to add and remove features so as to improve the classification accuracy. Additionally, seven different mathematical spirals are employed to replace the original spiral foraging pattern in the TSO algorithm. By adjusting the search scope of the spiral foraging strategy, this approach enhances the search performance of the algorithm and reduces the likelihood of getting trapped in local optima. The proposed algorithm was tested on 18 UCI datasets. The first set of experiments demonstrates the effectiveness of the somersault foraging strategy, ReliefF guiding strategy and variable spiral strategy. The RReTSO CY algorithm successfully reduces the average fitness value, achieves higher classification accuracy and selects fewer features. In the second set of experiments, RReTSO CY is compared with other binary swarm intelligence optimization algorithms, with results indicating that the proposed method effectively reduces the feature subset size, improves classification accuracy and achieves the lowest average fitness value. Finally, the Wilcoxon rank-sum tests were conducted to statistically validate the effectiveness of the proposed algorithm.

关键词： Feature selection Mathematical spiral ReliefF Somersault foraging tuna swarm optimization algorithm

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Optimizing FACTS devices location and sizing in integrated wind power networks using tuna swarm optimization algorithm

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2024年第13期149卷 7135-7153页

作者： Mohamed, Amal Amin Kamel, Salah Hassan, Mohamed H. Kamalov, Firuz Safaraliev, Murodbek Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Minist Elect & Renewable Energy Cairo 11517 Egypt Canadian Univ Dubai Dept Elect Engn Dubai 117781 U Arab Emirates Ural Fed Univ Dept Automated Elect Syst Ekaterinburg 620002 Russia

This study aims to address the problem of optimal power flow (OPF) in electrical networks by integrating wind power production FACTS devices. The main objectives of this study include minimizing generating costs, reducing power loss, enhancing the voltage profile of the system, and increasing its load ability. In this study, the tuna swarm optimization algorithm (TSO) is utilized as a novel meta-heuristic technique to optimize electrical networks to incorporate FACTS devices and stochastic wind energy production. To account for wind power, a model based on the Weibull probability density function is utilized to identify the optimal values of the decision variables. The study compares several objective functions, including minimization of fuel cost and active power loss across the transmission system, and simulates the test system consisting of TCSC, TCPS, and SVC using the IEEE 30-bus system as a network for examining system parameters. The efficacy of the TSO methodology is explored and compared to other traditional approaches in the paper. The simulation findings of the study show that by lowering the overall power cost and power losses, TSO is more successful in determining the OPF's ideal solution. The results show that the TSO algorithm performs better than driving training-based optimization (DTBO), Coulomb-Franklin's algorithm (CFA), and whale optimization algorithm (WOA) since it can handle more difficult OPF issues with a lower convergence rate. When comparing the four methods, for instance, TSO produced a noticeable improvement. It was able to successfully reduce the cost function to 807.454 $/h which is better than 807.9545 $/h for CFA, 809.4945 for DTBO, and 828.89 $/h for WOA, while lowering the power loss to 1.842 MW which is lower than the losses incurred by CFA, DTBO, and WOA, which are 1.9137 MW, 2.703 MW, and 2.757 MW, respectively. Furthermore, in Case 3, the gross cost reduced to 914.3735 $/h.

关键词： Wind power Optimal power flow FACTs devices Transmission line tuna swarm optimization algorithm

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An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

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TELECOMMUNICATION SYSTEMS 2024年第4期86卷 829-851页

作者： Chang, Yu He, Dengxu Qu, Liangdong Guangxi Minzu Univ Sch Math & Phys Nanning 530006 Guangxi Peoples R China Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China

tuna swarm optimization algorithm (TSO) is an innovative swarm intelligence algorithm that possesses the advantages of having a small number of adjustable parameters and being straightforward to implement, but the TSO exhibits drawbacks including low computational accuracy and susceptibility to local optima. To solve the shortcomings of TSO, a TSO variant based on behavioral evaluation and simplex strategy is proposed by this study, named SITSO. Firstly, the behavior evaluation mechanism is used to change the updating mechanism of TSO, thereby improving the convergence speed and calculation accuracy of TSO. Secondly, the simplex method enhances the exploitation capability of TSO. Then, simulations of different dimensions of the CEC2017 standard functional test set are performed and compared with a variety of existing mature algorithms to verify the performance of all aspects of the SITSO. Finally, numerous simulation experiments are conducted to address the optimization of wireless sensor network coverage. Based on the experimental results, SITSO outperforms the remaining six comparison algorithms in terms of performance.

关键词： tuna swarm optimization algorithm Behavior evaluation mechanism Simplex method Wireless sensor network

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Flexible job shop scheduling with stochastic machine breakdowns by an improved tuna swarm optimization algorithm

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 74卷 180-197页

作者： Fan, Chengshuai Wang, Wentao Tian, Jun Nankai Univ Tianjin 300350 Peoples R China

In job-shop production environments, machine breakdowns are a significant factor in reducing productivity. Existing approaches seldom consider algorithm improvement and rescheduling scheme design in an integrated manner, and lack stability considerations. This paper addresses the flexible job shop scheduling problem with random machine breakdowns, aiming to produce a stable rescheduling scheme that minimizes a combined index of maximum completion time and stability. The paper innovatively applies the tuna swarm optimization algorithm to the flexible job shop scheduling problem, proposing an efficient and superior improved version called the genetic chaos levy nonlinear tuna swarm optimization (GCLNTSO) algorithm. Three stability metrics are designed to guide the generation of efficient and stable rescheduling schemes. A rescheduling scheme is proposed that combines right-shift rescheduling with complete rescheduling. The proposed scheme is benchmarked against Brandymalter and Kacem's problems, and compared with other algorithms from the literature. The results demonstrate that the GCLNTSO algorithm outperforms other algorithms in terms of both performance and stability.

关键词： Flexible job-shop scheduling tuna swarm optimization algorithm Machine breakdowns Rescheduling

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IOT device type identification using magnetized Hopfield neural network with tuna swarm optimization algorithm

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swarm AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Muthukrishnan, A. Kamalesh, S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Science Dept Comp Sci & Engn 42 Avadi Vel Tech Rd Chennai 600062 Tamil Nadu India Velammal Coll Engn & Technol Dept Informat Technol Madurai 625009 India

Internet of Things (IoT) networks consist of physical devices connected to the Internet, embedded with actuators, sensors, and communication components that exchange data. To enhance IoT security, accurately identifying and assessing the safety of connected devices is essential. To improve IoT security, this research proposes the IoT Device Type Identification utilizing Memristor-based Magnetized Hopfield Neural Network with tuna swarm optimization algorithm (IOT-DTI-MHNN-TSOA). It includes data collection, feature extraction, IoT device type identification. In data collection, an actual network traffic dataset amassed through 10 various IoT device categories is used. In the feature extraction phase, optimal features such as TCP packets' time-to-live by server, packets' inter-arrival time by client, packets' inter-arrival time by server, TCP packets' time-to-live by client, packets' inter-arrival time, packet size, number of bytes sent and received, packet size by client, and total number of packets are extracted using a 2-Dimensional Flexible Analytic Wavelet Transform (2D-FAWT). These extracting features are provided to the IoT device type identification phase. In this phase, a Memristor-based Magnetized Hopfield Neural Network (MHNN) method is employed to perceive the categories of IoT device as known/seen or unknown/unseen categories. The tuna swarm optimization algorithm (TSOA) enhances the weight parameters of MHNN. The efficacy of the IOT-DTI-MHNN-TSOA classification framework is assessed using performance metrics, like precision, accuracy, F1-score, sensitivity, specificity, error rate, computational time, ROC, Computational Complexity. The IOT-DTI-MHNN-TSOA method provides higher accuracy of 99.97 %, higher sensitivity of 99.95 %, and higher precision of 99.92 % compared to the existing models.

关键词： Memristor based magnetized Hopfield neural network tuna swarm optimization algorithm 2-dimensional flexible analytic wavelet transform

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Coverage Control algorithm for Heterogeneous WSN based on Multi-objective tuna swarm optimization 23

Coverage Control Algorithm for Heterogeneous WSN based on Mu...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Peng Xu Shanshan Hu Yindi Yao Bozhan Zhao Xi'an Science and Technology Museum China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

In order to solve the coverage hole problem caused by random deployment of WSN nodes, aiming at the demand of energy consumption optimization in the process of redeployment, the coverage control problem is constructed as a multi-objective optimization problem, a coverage control algorithm for heterogeneous WSN with normal distribution of node perception radius is proposed. Firstly, in order to enrich the diversity of tuna population, the Sobol sequence was used for initialization in the initial stage. In addition, nonlinear weight coefficient is introduced to comprehensively consider the global search and local development ability of the algorithm. Secondly, the fast non-dominated sorting method is used for reference to classify the tuna population, and elite reservation and external archiving strategy are added to improve the accuracy of the algorithm. Then, a reasonable calculation method of crowding distance is proposed to enhance the diversity of optimal solutions. Finally, in the Matlab simulation environment, the NSMTSO, NSGA-II, NSMOFPA and MOPSO algorithm are tested in the standard test function and WSN coverage optimization respectively. The simulation results show that the NSMTSO algorithm proposed in this paper is more stable, and the optimal solution set obtained is more competitive in convergence and distribution.

关键词： Average mobile energy consumption Network coverage rate tuna swarm optimization algorithm Wireless sensor network

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Risk assessment study of hydrogen energy storage system based on KPCA-TSO-LSSVM

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 79卷 931-942页

作者： Liu, Jicheng Song, Yanan Yu, Xue North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

In order to improve the accuracy and efficiency of hydrogen energy storage system (HESS) risk assessment, the study proposes a risk portfolio assessment model based on kernel principal component analysis-tuna swarm optimization-least squares support vector machine (KPCA-TSO-LSSVM) algorithm. Firstly, the original data were downscaled using KPCA to extract principal components with at least 98% information content. These principal components use the extracted principal components as inputs to the model. Secondly, TSO is used to implement the optimization of parameter settings for LSSVM. Thirdly, the applicability of the proposed KPCA-TSO-LSSVM in HESS risk assessment is verified by case analysis. Finally, the superiority of the model proposed is verified by comparing TSO, whale optimization algorithm (WOA) and particle swarm optimization (PSO). The results show that KPCA-TSO-LSSVM performs optimally in HESS risk assessment. The classification time of the test sample is shorter at 0.0416 s. The accuracy is higher at 97%. Therefore, the proposed model can effectively identify HESS risks, reduce the operational risks of HESS, and improve the stability and reliability of the system.

关键词： Hydrogen energy storage system Risk assessment Kernel principal component analysis Support vector machine tuna swarm optimization algorithm

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Optimized fuzzy type 1 Mamdani controller to enhance transient stability of AC-power system

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WIRELESS NETWORKS 2024年第9期30卷 7617-7632页

作者： Reddy, V. Suryanarayana Kannan, A. S. Annamalai Univ Dept Elect Engn Chidambaram 608002 Tamil Nadu India Annamalai Univ Dept Elect Engn FEAT Chidambaram 608002 Tamil Nadu India

The dynamic behaviour of the system in power system networks is nonlinear. Due to heavy load and technical advancement, it faces several difficulties. One of the most significant factors that have a detrimental effect on the electrical network is instability in the voltage. By preserving the system voltage and removing voltage fluctuations, this work proposes the fuzzy Mamdani controller and increases voltage stability. The suggested fuzzy controller takes the place of the current PI regulator and allows for improved voltage regulation and disturbance compensation utilizing TSO (tuna swarm optimization). The team-based foraging behaviour of tuna swarms served as the primary source of motivation for TSO. The work uses two tuna swarm foraging strategies-spiral foraging and parabolic foraging-as models to create an efficient metaheuristic algorithm. TSO's performance is assessed concerning many actual technical problems. As a result, non-linear power quality issues are resolved by applying the static Var compensator (SVC)-based fuzzy Mamdani approach with the TSO algorithm. The IEEE-39 bus system is used to analyse and assess the recommended approach, and it is compared to previous research. The decrease of the error in the stable state of voltage at the buses and the highest possible voltage loss that takes place following the development of a defect established the effectiveness of the proposed approach for minimizing oscillations in voltage and harmonic flows. Simulations show how well the reactive power replacement and voltage regulation work using the fuzzy Mamdani controllers that have been built in MATLAB/SIMULINK.

关键词： Fuzzy type 1 Mamdani controller AC-power system tuna swarm optimization algorithm IEEE-39 bus system Voltage stability SVC controller

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TSO algorithm and DBN-Based Comprehensive Evaluation System for University Physical Education

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 426-435页

作者： Yang, Yonghua Hunan Commun Polytech Changsha 410132 Hunan Peoples R China

with the rise of fitness technologies and the integration of smart applications in education, improving physical education evaluation methods is essential for better assessing student performance inside and outside the classroom. Traditional evaluation methods often lack precision, fairness, and real-time capabilities. This study aims to develop an integrated evaluation method for university physical education using a combination of the tuna swarm optimization (TSO) algorithm and a Deep Belief Network (DBN) to optimize the accuracy and efficiency of evaluating both in-class and extracurricular physical activities. The evaluation system is built using the Campus Running APP, which tracks and analyzes student performance in various physical education aspects, including in-class participation, extracurricular activities, and fitness tests. The TSO algorithm is employed to optimize the DBN, improving its ability to process complex datasets and avoid local optima. The model is trained and tested on a dataset collected from student activity on the Campus Running APP. Experimental results show that the TSO-DBN model outperforms traditional methods, such as DBN, GWODBN, and FTTA-DBN, in terms of evaluation accuracy and processing time. The TSO-DBN model achieves a root mean square error (RMSE) of 0.2-0.3, significantly lower than the comparison models. Additionally, it reaches an R2 value of 0.98, indicating high prediction accuracy, and demonstrates the fastest evaluation time of 0.0025 seconds. These results underscore the assessments. The integration of the TSO algorithm with the DBN significantly improves the precision, efficiency, and fairness of physical education evaluations. The model offers a comprehensive and objective system for assessing student performance, helping universities better monitor and promote student health and physical activity. This approach paves the way for future research and application of AI-based systems in educational environments.

关键词： Campus fun run app integrated evaluation of university sports inside and outside the classroom tuna swarm optimization algorithm deep confidence network

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