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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 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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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

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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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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seagull optimization algorithm for solving real-world design optimization problems

MATERIALS TESTING

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MATERIALS TESTING 2020年第6期62卷 640-644页

作者： Panagant, Natee Pholdee, Nantiwat Bureerat, Sujin Yildiz, Ali Riza Sait, Sadiq M. Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia

In this research paper, a new surrogate-assisted metaheuristic for shape optimization is proposed. A seagull optimization algorithm (SOA) is used to solve the shape optimization of a vehicle bracket. The design problem is to find structural shape while minimizing structural mass and meeting a stress constraint. Function evaluations are carried out using finite element analysis and estimated by using a Kriging model. The results show that SOA has outstanding features just as the whale optimization algorithm and salp swarm optimization algorithm for designing optimal components in the industry.

关键词： seagull optimization algorithm control arm shape optimization structural design

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An Adaptive Fuzzy C Means with seagull optimization algorithm for Analysis of WSNs in Agricultural Field with IoT

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期126卷 1459-1480页

作者： Karunkuzhali, D. Meenakshi, Balasubramanian Lingam, Keerthi Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Gitam Univ Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

关键词： Clustering Griculture fields Environmental monitoring Fuzzy c means clustering seagull optimization algorithm

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A Multi-Objective Quantum-Inspired seagull optimization algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

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IEEE ACCESS 2022年 10卷 110497-110511页

作者： Wang, Peng Deng, Zhiliang Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.

关键词： Linear programming Metaheuristics Benchmark testing Qubit Pareto optimization Heuristic algorithms Spirals Autonomous aerial vehicles seagull optimization algorithm multi-objective problem qubits encoding path planning

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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

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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EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第2期12卷 571-596页

作者： Dhiman, Gaurav Singh, Krishna Kant Slowik, Adam Chang, Victor Yildiz, Ali Riza Kaur, Amandeep Garg, Meenakshi Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Delhi NCR Dept Elect & Commun Engn KIET Grp Inst Ghaziabad India Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Uludag Univ Dept Automot Engn Coll Engn TR-16059 Bursa Turkey Sri Guru Granth Sahib World Univ Dept Comp Sci & Engn Fatehgarh Sahib Punjab India

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective seagull optimization algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence.

关键词： seagull optimization algorithm Multi-objective optimization Evolutionary Pareto Engineering Design Problems Convergence Diversity

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