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A New Hybrid seagull optimization algorithm for Feature Selection

IEEE ACCESS 2019年 7卷 49614-49631页

作者： Jia, Heming Xing, Zhikai Song, Wenlong Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

关键词： Feature selection hybrid optimization seagull optimization algorithm thermal exchange optimization

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Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm

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ENERGY REPORTS 2019年 5卷 1616-1625页

作者： Cao, Yan Li, Yiqing Zhang, Geng Jermsittiparsert, Kittisak Razmjooy, Navid Xian Technol Univ Sch Mechatron Engn Xian 710021 Shaanxi Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Tsfresh Univ Dept Elect Engn Tafresh Iran

This study proposes an optimal model to design and simulate the proton exchange membrane fuel cell (PEMFC) systems. The purpose of this paper is to present an improved version of seagull optimization algorithm for optimal parameter identification of the PEMFC stacks. The new algorithm uses the Levy flight mechanism to give faster convergence rates. The sum of the squared error between the empirical values and achieved optimal model is analyzed based on two empirical PEMFC models including BCS 500-W and NedStack PS6. This analysis is performed to show the potential of the presented method by considering different conditions. Simulation results are compared with several optimization algorithms and show the algorithm's superiority in terms of the solutions quality and the convergence speed. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： seagull optimization algorithm Levy flight Proton exchange membrane fuel cells Modeling

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optimization of WSN localization algorithm based on improved multi-strategy seagull algorithm

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TELECOMMUNICATION SYSTEMS 2024年第3期86卷 547-558页

作者： Yu, Xiuwu Liu, Yinhao Liu, Yong Univ South China Hengyang 421000 Hunan Peoples R China Shenzhen Univ Shenzhen 518000 Peoples R China

This paper proposes a multi-strategy modified seagull algorithm to optimize DV-Hop localization algorithm (DISO) to improve the precision of non-range-ranging localization algorithm in wireless sensor networks. Firstly, the algorithm analyzes the causes of errors in the positioning of the traditional non-ranging location algorithm DV-Hop, and improves these steps. Among them, the communication area of anchor nodes is divided by different radii, so as to reduce the influence of distance on hop number. The node distribution is stochastic, so the mean square error is used instead of the unbiased estimation, and the weight is introduced to calculate the average jump distance, which reduces the error caused by the random distribution of nodes. Secondly, the objective function optimization method is used to replace the trilateral measurement, and the improved seagull optimization algorithm is used for iterative optimization. Finally, the seagull optimization algorithm is modified in view of its shortcomings. The chaotic mapping was used to initialize the seagull population and increase its diversity. The flight parameters of seagull and the position update methods of the worst and best seagull are improved, and the optimization ability of the algorithm is improved by combining levy flight mechanism and T distribution variation strategy. The simulation results show that the initial population distribution of DISO algorithm is more uniform, which establishes a basic advantage for the subsequent optimization. Keeping the other parameters consistent, DISO algorithm has higher positioning accuracy than other comparison algorithms, no matter changing the number of anchor nodes or the total number of nodes or changing the communication radius. The positioning errors of DISO algorithm are reduced by 45.63%, 17.17%, 22.61% and 11.68% compared with DV-Hop algorithm and other comparison algorithms under different number of anchor nodes. Under different total number of nodes, the pos

关键词： Wireless sensor network Range-free location seagull optimization algorithm Chaotic mapping

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seagull-Cuckoo Search algorithm for Function optimization 6

Seagull-Cuckoo Search Algorithm for Function Optimization

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6th International Conference for Convergence in Technology (I2CT)

作者： Das, Gyanesh Panda, Rutuparna VSSUT Dept ECE Burla 768018 Odisha India

ISBN: (纸本)9781728188768

This paper anticipates to frame a hybrid seagull optimization algorithm - Cuckoo Search (SOA-CS) algorithm for objective function minimization. The migration and attacking strategies of seagulls are complimented by the social breeding behaviour of cuckoo birds. In addition, the thought of Levy flight walking methodology is also inherited. Twenty-three standard benchmark test functions are used for validation. To figure out the outcomes - the proposed algorithm converges faster and provides better results over both the SOA and CS algorithms.

关键词： seagull optimization algorithm Cuckoo Search Levy Flight

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Short term wind speed prediction based on CEESMDAN and improved seagull optimization kernel extreme learning machine

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-33页

作者： Qin, Xiwen Yuan, Liping Dong, Xiaogang Zhang, Siqi Shi, Hongyu Changchun Univ Technol Sch Math & Stat Changchun 130012 Peoples R China Jilin Prov Key Lab Data Sci & Intelligent Decis Changchun 130012 Peoples R China

Accurate wind speed predictions are crucial for the planning, operation, and energy management of wind farms. In this paper, we propose a novel wind speed prediction model, CEESMDAN-LNR-SOA-KELM. Firstly, we employ the CEESMDAN decomposition method to extract features from the original wind speed data, capturing the underlying characteristics of the data. Secondly, we apply a nonlinear treatment to the convergence factor A of the seagull optimization algorithm (SOA) to better adapt to the complexity and diversity of the problem, thereby enhancing the algorithm's convergence speed. Additionally, we introduce a random opposition-based learning strategy to effectively prevent the SOA algorithm from getting stuck in local optima. We further optimize the parameters of KELM using LNR-SOA. The results of function optimization demonstrate that the proposed improvement strategy significantly enhances the parameter optimization capability of the SOA algorithm. The wind speed data from the Sotavento Galicia wind farm in Spain were used as the subject of the numerical experiments. The experimental results indicate that the model proposed in this paper demonstrates higher accuracy and reliability in wind speed prediction compared to the comparative models. It provides an effective forecasting tool for the wind energy industry and meteorological predictions.

关键词： Wind speed prediction Adaptive decomposition Random opposition-based learning seagull optimization algorithm Kernel extreme learning machine

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Multi-Role collaborative framework for structural damage identification considering measurement noise effect

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MEASUREMENT 2025年 250卷

作者： Chen, Zepeng Zhang, Zhiyu Chen, Xiangmei Hou, Rongrong Ding, Zhenghao Liu, Feng Yang, Zhicheng Foshan Univ Sch Civil Engn & Transportat Foshan Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Kyoto Univ Dept Civil & Earth Resources Engn Kyoto Japan Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Zhongkai Univ Agr & Engn Coll Urban & Rural Construct Guangzhou Peoples R China

Swarm intelligence has been extensively applied in structural damage identification, but a single method may not perform well in identification, especially using limited and noised vibration data. In this context, the objective landscape of the formulated identification problem is often ill-posed, indicating the optimized landscape is filled with many local optimal points. If the algorithm gets trapped in local optimal points, it will not obtain satisfactory identification results. To address this issue, this study introduces the sparse regularization technique to construct a well-posed objective function. Furthermore, a novel multi-role collaborative framework is proposed, which integrates different swarm intelligent and enables the individual in the algorithm to switch different roles, meaning employing different updating strategies, for the demands of different identification cases. Therefore, a more accurate identification results can be obtained. A series of numerical simulations and a laboratory validation on a box-section beam with multiple notches are carried out. The features of multi-role adaptive mechanism and diversity search strategies in the proposed framework guarantee its advantages and superiority on obtaining better identifications compared with single swarm intelligence algorithm, providing a new way in developing high-efficiency model updating and damage detection algorithms.

关键词： Multi-role collaborative framework seagull optimization algorithm Sine cosine algorithm Sparse regularization Noise effect

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Energy Consumption Prediction and optimization of Electric Vehicles Based on RLS and Improved SOA

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IEEE ACCESS 2024年 12卷 38180-38191页

作者： Liu, Chunling Changchun Univ Technol Inst Automot Engn Coll Humanities & Informat Changchun 130000 Peoples R China

The new energy vehicle industry is facing new challenges. To predict and optimize the energy consumption of electric vehicles, this study predicts energy consumption based on the energy consumption characteristics of the electric vehicle power system and air conditioning system, and combines path optimization algorithms for energy-saving path planning. The study first improves the recursive least squares algorithm by combining the forgetting factor, and constructs a vehicle energy consumption identification model based on the improved recursive least squares algorithm and neural network. Then, a path optimization model based on improved seagull optimization is established using chaotic mapping strategy and t-distribution to improve the seagull optimization algorithm. The results showed that the predicted final energy consumption of the model constructed in the study was 2.81kW.h, with an error rate of 5.1%. The improved seagull optimization algorithm obtained an optimal solution of 30.88m for burma14 and 423.74m for oliver30, which were consistent with the published optimal solutions. When the air conditioning was turned on, the energy consumption of the path selected by the algorithm was reduced by about 5.6%. Under the condition of not turning on the air conditioning, the energy consumption of the path selected by the algorithm was reduced by about 4.98%. In summary, the model constructed through research has good application effects in predicting and optimizing vehicle energy consumption. The contribution of the research lies in it helps to reveal the laws of energy utilization in electric vehicles, improve the economy, safety, and environmental friendliness of electric vehicles during operation, and promote the overall management of new energy vehicles.

关键词： Recursive least squares algorithm seagull optimization algorithm vehicle energy consumption neural network path planning

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Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第10期21卷 2281-2298页

作者： Dai, Yiyan Zhang, Mingjin Xin, Xu Chen, Xiaohu Li, Yongle Liu, Maoyi Southwest Jiaotong Univ Dept Bridge Engn Chengdu Peoples R China Southwest Jiaotong Univ State Key Lab Bridge Intelligent & Green Construct Chengdu Peoples R China TY Lin Int Engn Consulting Chongqing Co Ltd Dept Bridge Business Chongqing Peoples R China Chongqing Construct Investment Grp Co Ltd Dept Sci & Technol Chongqing Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu 610031 Peoples R China

Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and seagull optimization algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

关键词： Wind speed forecasting intelligent algorithms Variational mode decomposition Spotted Hyena Optimizer Kernel extreme learning machine seagull optimization algorithm

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Optimum PV reconfiguration approach based on SOA for improving the harvest power under PS situations

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ELECTRICAL ENGINEERING 2024年第1期106卷 869-889页

作者： Mohanapriya, V. Sharmila, B. Bannari Amman Inst Technol Dept EEE Erode Tamil Nadu India Sri Ramakrishna Engn Coll Dept EIE Coimbatore Tamil Nadu India

Photovoltaic (PV) systems-based harvest energy becomes huge support for the electric grids in recent days and also becomes the sustainable replacement for conventional energy sources. But the efficacy of PV-based electricity is degraded due to the effect of partial shading conditions. The reconfiguration technique is one effective strategy to diminish the impact of shadow on the photovoltaic (PV) array. This entails rearranging the PV array's switching matrix by the temperature and shading levels. A seagull optimization algorithm (SOA) is proposed in this work to identify the optimal pattern for rearranging the matrix and optimising power output from the PV array. A 9x9 PV array operating under four common shadow patterns-Short Wide (SW), Long Wide (LW), Short Narrow (SN), and Long Narrow (LN)-is subjected to the algorithm. Mismatch power, fill factor, percentage power loss, and percentage power enhancement are used to assess the algorithm's effectiveness. Comparisons are made between the results from the proposed SOA and those from the Total-Cross-Tied (TCT), Competence Square, Butterfly Optimisation algorithm, and Harris Hawks optimization. The highest increase in Global Maximum Power has been attained by the proposed SOA corresponding to the TCT array is 36% and 30% in the SW and LW shadow arrangement, while the least increase of 7% occurs in the SN and LN arrangements. The evaluated results show the proposed SOA's competence and effective in optimising the shaded array reconfiguration.

关键词： Photovoltaic system Reconfiguration seagull optimization algorithm Partial shading conditions Global maximum power

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SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Yaqoob, Abrar Verma, Navneet Kumar Mir, Mushtaq Ahmad Tejani, Ghanshyam G. Eisa, Nashwa Hassan Babiker Osman, Hind Mamoun Hussien Shah, Mohd Asif VIT Bhopal Univ Sch Adv Sci & Language Bhopal 466114 India Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Saveetha Univ Saveetha Dent Coll & Hosp Saveetha Inst Med & Tech Sci Dept Res Analyt Chennai 600077 India King Khalid Univ Coll Appl Med Sci Dept Clin Lab Sci Abha 61421 Saudi Arabia Kardan Univ Dept Econ Parwane Du Kabul 1001 Afghanistan Lovely Profess Univ Div Res & Dev Phagwara 144001 Punjab India

In this study, we propose a novel approach for breast cancer classification that integrates the seagull optimization algorithm (SGA) for feature selection with the Random Forest (RF) classifier for effective data classification. The novelty of our approach lies in the first-time application of SGA for gene selection in breast cancer diagnosis, where SGA systematically explores the feature space to identify the most informative gene subsets, thereby improving classification accuracy and reducing computational complexity. The selected features are subsequently classified using RF, known for its robustness and high accuracy in handling complex datasets. To evaluate the effectiveness of the proposed method, we compared it with other classifiers, including Linear Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The proposed SGA-RF combination achieved a best mean accuracy of 99.01% with 22 genes, outperforming other methods and demonstrating consistent performance across varying feature subsets. The mean accuracies ranged from 85.35 to 94.33%, highlighting a balance between feature reduction and classification accuracy. Future work will explore the integration of other nature-inspired algorithms and deep learning models to further enhance performance and clinical applicability.

关键词： seagull optimization algorithm Random forest Cancer classification High dimensional data

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