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Planar-mirror reflection imaging learning based seagull optimization algorithm for global optimization and feature selection

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KNOWLEDGE-BASED SYSTEMS 2025年 317卷

作者： Long, Wen Jiao, Hui Yang, Yang Xu, Ming Tang, Mingzhu Wu, Tiebin Guizhou Univ Finance & Econ Sch Math & Stat Guiyang 550025 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Changsha Univ Sci & Technol Sch Energy Power & Engn Changsha 410114 Peoples R China Hunan Univ Humanities Sci & Technol Sch Energy & Elect Engn Loudi 417000 Peoples R China

seagull optimization algorithm (SOA) exhibits certain weaknesses such as poor accuracy and a tendency to stagnate in local optimal solutions when solving complex optimization problems. This paper suggests an enhanced variant of SOA, referred to as planar-mirror reflection imaging learning based SOA (PRIL-SOA), to address these limitations. First, we present the novel nonlinear strategies for adjusting the employing variable A and control parameter B are presented to achieve a balance between global and local search capabilities. Second, a modified position update equation is devised that incorporates velocity components and personal history best positions, thereby enhance solution precision. Third, a new PRIL strategy is introduced to maintain diversity and prevent premature convergence. To validate the performance of PRIL-SOA, we conduct a series of benchmark tests, including 23 classical functions and a feature selection problem involving 21 datasets are used. The results indicate that PRIL-SOA consistently outperforms basic SOA and other meta-heuristics. The average search success rate of PRIL-SOA on benchmark test problems is 91.3 %, with 21 out of 23 problems achieving the theoretical optimal value. Compare with SOA, mountain gazelle optimizer (MGO), whale optimization algorithm (WOA), hunger games search (HGS), HHO-based joint opposite selection (HHO-JOS), modified SCA (MSCA), and exploration-enhanced GWO (EEGWO), the average success rates of PRIL-SOA is better to 86.95 %, 78.26 %, 82.61 %, 65.22 %, 56.52 %, 60.87 %, and 4.35 %, respectively.

关键词： seagull optimization algorithm Gaussian function Function optimization Feature selection

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A Method for the Front-End Design of Electric SUVs Integrating Kansei Engineering and the seagull optimization algorithm

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ELECTRONICS 2025年第8期14卷 1641-1641页

作者： Zhang, Yutong Wu, Jiantao Sun, Li Wang, Qi Wang, Xiaotong Li, Yiming Yanshan Univ Sch Arts & Design Qinhuangdao 066000 Peoples R China Yanshan Univ Coastal Area Port Ind Dev Collaborat Innovat Ctr Qinhuangdao 066000 Peoples R China

With the rapid expansion of the Electric Sport Utility Vehicle (ESUV) market, capturing consumer aesthetic preferences and emotional needs through front-end styling has become a key issue in automotive design. However, traditional Kansei Engineering (KE) approaches suffer from limited timeliness, subjectivity, and low predictive accuracy when extracting affective vocabulary and modeling the nonlinear relationship between product form and Kansei imagery. To address these challenges, this study proposes an improved KE-based ESUV styling framework that integrates data mining, machine learning, and generative AI. First, real consumer reviews and front-end styling samples are collected via Python-based web scraping. Next, the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) are used to extract representative Kansei vocabulary. Subsequently, the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models are constructed and optimized using the seagull optimization algorithm (SOA) and Particle Swarm optimization (PSO). Experimental results show that SOA-BPNN achieves superior predictive accuracy. Finally, Stable Diffusion is applied to generate ESUV design schemes, and the optimal model is employed to evaluate their Kansei imagery. The proposed framework offers a systematic and data-driven approach for predicting consumer affective responses in the conceptual styling stage, effectively addressing the limitations of conventional experience-based design. Thus, this study offers both methodological innovation and practical guidance for integrating affective modeling into ESUV styling design.

关键词： kansei engineering seagull optimization algorithm back propagation neural network support vector regression stable diffusion model front-end styling of the ESUV

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A novel fault location method for distribution networks with distributed generators based on improved seagull optimization algorithm

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ENERGY REPORTS 2025年 13卷 3237-3245页

作者： Li, Yuan Su, Shi Hu, Faping He, Xuehao Su, Jiali Zhang, Jing Li, Botong Liu, Sumei Man, Wenshuo Yunnan Elect Power Grid Co Ltd Elect Power Res Inst Kunming 650217 Peoples R China Yunnan Power Grid Co Ltd Honghe Power Supply Bur Mengzi 661000 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China

With the increasing penetration of distributed generators (DGs), the fault current of the connected distribution network (DN) becomes complex and variable. The overcurrent signals at the same location may be decided by the connected grid or solely by the DGs. As a result, the fault location methods based on intelligent optimization algorithms have low accuracy and poor fault tolerance, especially when the overcurrent signals are determined only by the DGs under simultaneous multi-area faults. Aimed at the aforementioned problems, a novel fault location method based on an improved seagull optimization algorithm is proposed for the distribution grid integrated with the DGs. The expression of the switch status function for DNs is firstly improved considering the impact of DGs on the overcurrent signals. An elite reverse learning strategy is introduced for the seagull optimization algorithm to the diversity of the initial seagull population. Both the Levy flight control and random walk strategies are used to increase the randomness of the optimization algorithm. It is good for avoiding the emergence of locally optimal results due to the variable overcurrent status of the feeder terminal units (FTUs). Finally, the proposed fault location method was validated using a simulation model of an active DN with photovoltaic DGs based on the IEEE 33 nodes. Based on the simulation results, it is verified that the proposed fault location method can identify single-point or multi-point faults in the case of distorted overcurrent signals. The proposed method is superior to the existing one in both high accuracy and high fault tolerance.

关键词： Fault location seagull optimization algorithm Improved switch function Distribution networks Distributed generators

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Hybrid Strategies Based seagull optimization algorithm for Solving Engineering Design Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-20页

作者： Hou, Pingjing Liu, Jiang Ni, Feng Zhang, Leyi Univ Shanghai Sci & Technol Business Sch 334 Jungong Rd Shanghai 200093 Peoples R China

The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.

关键词： Constrained optimization seagull optimization algorithm Hybrid strategies Engineering design problem

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Enhanced Coconut Yield Prediction Using Internet of Things and Deep Learning: A Bi-Directional Long Short-Term Memory Lévy Flight and seagull optimization algorithm Approach

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7516页

作者： Alkhawaji, Rami N. Serbaya, Suhail H. Zahran, Siraj Vita, Vasiliki Pappas, Stylianos Rizwan, Ali Fotis, Georgios Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk 48322 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Ind Engn Jeddah 21589 Saudi Arabia Univ Business & Technol Dept Ind Engn Jeddah 23847 Saudi Arabia ASPETE Sch Pedag & Technol Educ Athens Dept Elect & Elect Engn Educators Iraklion 14121 Greece Merchant Marine Acad Aspropyrgos Dept Engn Aspropyrgos 19300 Greece Aarhus Univ Ctr Energy Technol Birk Centerpk 15 DK-7400 Herning Denmark

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated L & eacute;vy Flight and seagull optimization algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R2) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut

关键词： bi-directional long short-term memory coconut yield estimation internet of things least absolute shrinkage and selection operator L & eacute vy flight seagull optimization algorithm

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optimization scheduling for multi-source water distribution systems in mountainous region based on seagull optimization algorithm

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MODERN PHYSICS LETTERS B 2025年第13期39卷

作者： Wang, Dongrui Chen, Hongxun Ma, Zheng Yi, Bobo Shanghai Univ Shanghai Inst Appl Math & Mech Sch Mech & Engn Sci 149 Yanchang Rd Shanghai 200072 Peoples R China China Ship Sci Res Ctr 185 Gaoxiong Rd Shanghai 200011 Peoples R China

Multi-source water distribution systems (WDSs) are critical to solving the increasing demand for urban water supply. Appropriate management of limited resources necessitates optimization of water scheduling in order to reduce energy consumption. However, certain complexities of applying such systems bring severe challenges to optimal scheduling methods, exemplified in mountain regions, where larger elevation gradients make distribution more complicated than in plain regions. Therefore, this study attempts to present best practices in how to reduce the energy consumption of water supply, especially in complex mountainous regions, through innovation of optimal scheduling methods. Based on the seagull optimization algorithm (SOA), a systematic optimization scheduling method for multi-source WDSs is proposed. The optimization results are compared with those obtained from the genetic algorithm. A case study of such optimization in the mountainous region of C-County, China is presented. Power consumption prior and post optimization is compared. The results show that this optimization scheduling method is both effective and feasible. Annual power consumption can be reduced by significant amounts, savings of 23.3% in this case study, and the optimal solution can be deployed with 40 iteration steps.

关键词： Water distribution system optimization scheduling multi-source seagull optimization algorithm

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Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

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SOFT COMPUTING 2023年第7-8期28卷 6403页

作者： Samal, Padarbinda Panigrahy, Damodar KIIT Deemed Be Univ Dept Elect Engn Bhubaneswar 751024 Orissa India SRM Inst Sci & Technol Dept Elect & Commun Engn Kattankulathur Tamil Nadu India

This paper introduces a planning mechanism for optimal feeder reconfiguration, DSTATCOM allocation, and sizing in unbalanced radial distribution systems. The minimization of total active power losses (TACPL) is considered as the main objective of this work. A three phase forward-backward sweep load flow algorithm is utilized as a subroutine for the computation of the TACPL. The seagull optimization algorithm (SOA) is used as the solution methodology for this mixed integer non-linear planning problem. The simultaneous feeder reconfiguration, DSTATCOM installation obtains significant reduction in in the 19- and 25- bus unbalanced distribution network. Furthermore, the bus voltage magnitudes (pu) is also found to be enhanced. The SOA obtains lower TACPL in comparison to some of the well-known optimization algorithms like, differential evolution algorithm (DEA), moth-flame optimization (MFO) algorithm, and Jaya optimization algorithm (JOA). The proposed technique also attains lower TACPL in comparison to some of the established techniques reported in the literature while determining the optimal location, and rating of the DSTATCOM considering TACPL minimization.

关键词： Unbalanced radial distribution systems Three phase load flow DSTATCOM Feeder reconfiguration seagull optimization algorithm

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An enhanced hybrid seagull optimization algorithm with its application in engineering optimization

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ENGINEERING WITH COMPUTERS 2023年第2期39卷 1653-1696页

作者： Hu, Gang Wang, Jiao Li, Yan Yang, MingShun Zheng, Jiaoyue Xian Univ Technol Sch Mech & Precis Instrument Engn 5 South Jinhua Rd Xian 710048 Peoples R China Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China

Aiming at the problems such as slow search speed, low optimization accuracy, and premature convergence of standard seagull optimization algorithm, an enhanced hybrid strategy seagull optimization algorithm was proposed. First, chaos mapping is used to generate the initial population to increase the diversity of the population, which lays the foundation for the global search. Then, a nonlinear convergence parameter and inertia weight are introduced to improve the convergence factor and to balance the global exploration and local development of the algorithm, so as to accelerate the convergence speed. Finally, an imitation crossover mutation strategy is introduced to avoid premature convergence of the algorithm. Comparison and verification between MSSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective and its incomplete algorithms all improve SOA to different degrees in both exploration and exploitation. 25 classic functions and the CEC2014 benchmark functions were tested, and compared with seven well-known meta-heuristic algorithms and its improved algorithm to evaluate the validity of the algorithm. The algorithm can explore different regions of the search space, avoid local optimum and converge to global optimum. Compared with other algorithms, the results of non-parametric statistical analysis and performance index show that the enhanced algorithm in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. At the same time, in order to prove its applicability and feasibility, it is used to solve two constrained mechanical engineering design problems contain the interpolation curve engineering design and the aircraft wing design. The engineering curve shape with minimum energy, minimum curvature, and the smoother shape of airfoil with low drag are obtained. It is proved that enhanced

关键词： seagull optimization algorithm Chaotic mapping Nonlinear strategy Inertial weight Variation operation Imitation quantum crossover

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Sign Language Recognition using Improved seagull optimization algorithm with Deep Learning Model 2

Sign Language Recognition using Improved Seagull Optimizatio...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Sivaraman, R. Santiago, S. Chinnathambi, K. Sarkar, Swagata Sangeethaa, S. N. Srimathi, S. Dwaraka Doss Goverdhan Doss Vaishnav Coll Dept Math Chennai 600106 Tamil Nadu India St Josephs Coll Autonomous Dept Comp Sci Trichy 620002 India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Comp Sci & Engn Chennai Tamil Nadu India Sri Sairam Engn Coll Artificial Intelligence & Data Sci Chennai 44 Tamil Nadu India Bannari Amman Inst Technol Comp Sci & Engn Sathyamangalam Tamil Nadu India SIMATS Saveetha Sch Engn Dept Bio Technol Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Sign Language Recognition (SLR) covers the ability to translate Sign Language (SL) signals into written or spoken languages. This technique is useful for hearing-impaired people by offering them an effective method to communicate with persons having trouble in recognizing SLs. It can also be employed for generating automatic captions in real-time for live actions and videos. Various models of SLR include machine and deep learning, and computer vision techniques. A commonly employed technique involves using a camera to capture the body and hand movements of the signer, processing the video data to identify the signs. One of the high tasks of SLR includes the flexibility in SL over numerous individuals and cultures, the complex of definite signs, and the need for real-time process. This manuscript presents a SL Recognition Using an Improved seagull optimization algorithm with Deep Learning (SLR-ISOADL) methodology. The SLR-ISOADL approach aims to exploit a hyperparameter-tuned DL model to recognize and classify the SLs. In the SLR-ISOADL approach, a bilateral filtering (BF) approach can be applied to get rid of the noise. For learning and deriving intrinsic patterns, the SLR-ISOADL approach employs the AlexNet model. Besides, the ISOA can be applied for optimal hyperparameter election of the AlexNet model. Finally, the multilayer perceptron (MLP) technique can be exploited to detect and classify the SLs. The analytical experiment of the SLR-ISOADL technique is conducted on a benchmark dataset. The investigational analysis highlighted that the SLR-ISOADL technique gains enhanced detection outcomes in terms of distinct measures.

关键词： Sign Language Recognition seagull optimization algorithm Deep Learning AlexNet Multilayer Perceptron

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Research on emergency vehicle routing method in emergency material support stage based on seagull optimization algorithm 24

Research on emergency vehicle routing method in emergency ma...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Zhang, Bo Ding, Mengmeng Li, Qiaochu Shenyang Univ Sch Informat Engn Shenyang 110044 Liaoning Peoples R China

ISBN: (纸本)9798400709784

In the stage of emergency supplies reserve, in order to ensure the smooth implementation of the emergency supplies work plan, the problem of vehicle routing for emergency vehicles is studied. Firstly, a vehicle routing model for emergency vehicles with the lowest total cost including carbon emission cost is constructed. Secondly, a strategy is introduced to control parameter A by incorporating a nonlinear decreasing inverse S-shaped function into the attack behavior part of the basic seagull optimization algorithm, and the seagull optimization algorithm is improved. Finally, based on the classic Solomon test data, random data is selected to verify the algorithm and model, and compared with the basic seagull optimization algorithm. The experimental results show that the improved seagull optimization algorithm has better convergence than the basic seagull optimization algorithm, indicating that the model and algorithm have good applicability and practical value.

关键词： Emergency supplies seagull optimization algorithm Vehicle routing

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