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Solid particle erosion prediction in elbows based on machine learning and swarm intelligence algorithm

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第0期218卷

作者： Wang, Zhiguo Chen, Haoyu Wang, Meng Zhang, Xu Dou, Yihua Xian Shiyou Univ New Energy Coll Xian Key Lab Wellbore Integr Evaluat Xian 710065 Peoples R China Res Inst Petr Explorat & Dev PetroChina Beijing 100083 Peoples R China

Continuous impact of solid particles causes severe pipeline wear, and may result in leakage in directional change areas such as elbows. Accurate prediction of erosion is essential in the petroleum industry. In this study, a new machine learning (ML)-based model for predicting elbow erosion was established. Seven parameters were selected as feature inputs from the fluid characteristics, particle characteristics, and pipe characteristics. The maximum erosion rate was used as the predicted output. Based on the gas-solid flow data, the prediction ac-curacy of different ML models was compared. The kernel extreme learning machine (KELM) model was considered as the optimal model. For gas-liquid-solid flow, incorporating swarm intelligence (SI) algorithms, the whale optimization algorithm-hybrid kernel extreme learning machine (WOA-HKELM) erosion prediction model was proposed;the predictions were compared with the experimental values. The root mean square error (RMSE) of the prediction was 0.82 x 10-3, which is consistent with the experimental results. It was also demonstrated that the model can capture the trend of the influence of mixed dimensionless inputs on the prediction results in churn and annular flow.

关键词： Elbow Erosion Multiphase flow swarm intelligence algorithm Hybrid kernel extreme learning machine

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Application of data mining based on swarm intelligence algorithm in financial support of livestock and poultry breeding insurance

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SOFT COMPUTING 2023年

作者： Wang, Yuanyuan Hainan Normal Univ Sch Econ & Management Haikou 571127 Hainan Peoples R China

Among the components of China's actual economic structure, aquaculture has always been a key project, and as a part of agriculture, it has been greatly developed. However, there are still many problems in the actual breeding process, such as low financial support, low information transparency, high financing difficulty and lack of channels, high claims costs and serious moral hazard in the livestock and poultry breeding insurance field, which will affect the development and progress of the industry. Therefore, in this context, this paper improves the efficiency analysis of financial support for livestock and poultry breeding insurance by introducing data mining and swarm intelligence algorithms. Then use the analysis data to build a model to further evaluate the efficiency of financial support. The construction of the model is roughly divided into four parts: insurance density, insurance depth, insurance amount and income. The weight of different indicators is calculated through the analytic hierarchy process, and the overall ranking results are output through consistency test. Some data of the above models are used as the output indicators of DEA decision-making units for top-down ranking. The result analysis shows that the development of technology in this industry can effectively improve the actual productivity of agricultural crops, thus effectively promoting the development of local agriculture. In this paper, data mining and swarm intelligence algorithms are introduced into livestock and poultry breeding insurance to complete the analysis and evaluation of financial support.

关键词： swarm intelligence algorithm Data mining Livestock and poultry breeding insurance Financial support

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Performance enhanced geometrically shaped 32-ary quadrature amplitude modulation enabled by swarm intelligence algorithm

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OPTICAL ENGINEERING 2023年第5期62卷 058101-058101页

作者： Yan, Xu Ma, Jie Liu, Jianfei Lu, Jia Zeng, Xiangye Luo, Mingming Hebei Univ Technol Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin Peoples R China

The geometrically shaped 32-ary quadrature amplitude modulation (GS-32QAM) signal enabled by swarm intelligence algorithm is proposed in high-speed and long-haul coherent optical communication system. The geometric shaping (GS) is obtained by constructing a generic constellation optimization scheme applying particle swarm algorithm (PSO), wolf pack algorithm (WPA), and marine predator algorithm (MPA), respectively. The complexity of three algorithms applied to GS-32QAM is systematically analyzed, where MPA can obtain better search results with slightly higher complexity than PSO. The results show that the optimized constellation is significantly better than the uniform signal in terms of reducing bit error rate and increasing generalized mutual information (GMI). The maximum GMI gain of GS signal with PSO and WPA are both 0.25 bit / sym, and the maximum GMI gain of GS signal with MPA is up to 0.3 bit / sym. At the hard-decision forward error correlation limitation of 3.8 x (10 - 3), compared with uniform signal, the GS signal with PSO, WPA, and MPA can provide the optical signal-to-noise ratio gain of 1, 1.1, and 1.6 dB tolerance, respectively. In terms of data rate, compared with uniform signal, three GS signals can increase by 33, 36, and 43 Gbit / s, respectively.

关键词： swarm intelligence algorithm geometrically shaped 32-ary quadrature amplitude modulation generalized mutual information optical signal-to-noise ratio

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THE OPTIMAL SELECTION OF IDEOLOGICAL AND POLITICAL ISSUES IN BUSINESS COURSES BASED ON swarm intelligence algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2023年第3期24卷 409-417页

作者： Yin, Xuecai Zhoukou Vocat & Tech Coll Zhoukou 466000 Peoples R China

The current optimal selection matrix for ideological and political issues of business courses is mostly set as a single objective form, and the topic selection is limited in scope, increasing the mutation rate of the optimal selection of topics. Therefore, the design and analysis of the optimal selection method for ideological and political issues of business courses based on the swarm intelligence algorithm is proposed. According to the actual measurement needs and standards, extract the optimal characteristics of ideological and political issues selection of the curriculum, use a multi-level approach to break the limits of issues selection, establish a multi-level swarm intelligence selection matrix, build an optimal selection model for ideological and political issues of business and trade courses based on swarm intelligence accounting, and achieve the optimal selection of issues through group fixed-point optimization. The novelty of this work lies in the design and analysis of the optimal selection method for ideological and political issues in business courses using a swarm intelligence algorithm. This approach introduces a new way of selecting topics by harnessing the power of collective intelligence inspired by the behavior of insects or animals. The final test results show that the mutation rate of the optimal selection of the three topics finally screened using the swarm intelligence algorithm is better controlled below 0.2 through the measurement of five classes, indicating that the topic is more practical, more targeted, and has better discussion value.

关键词： swarm intelligence algorithm Commerce and trade Professional courses Curriculum ideological and political Ideological and political issues Optimal solution

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Application of swarm intelligence algorithm on PFSP

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International English Education Research 2017年第2期 52-55页

作者： LI Minnan LIU Sheng School of Management Shanghai University of Engineering Science Shanghai 200620 China

With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more.

关键词： PFSP swarm intelligence algorithm combinatorial optimiTafion industrial manufacturing

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A New Discrete Eel swarm intelligence algorithm

A New Discrete Eel Swarm Intelligence Algorithm

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2014 4th IEEE International Conference on Information Science and Technology

作者： SUN Yao-sheng HUANG Zhang-can CHEN Yu YUAN Chao School of Science Wuhan University of Technology

Inspired by the migration behaviours of eels, we propose a new discrete eel swarm intelligence algorithm in this paper. First, this paper analyzes the behaviours of eels, then establishes a mathematical model of the important behaviour being extracted. Based on rational organization of three important behaviours of eel, namely concentration adaptation, neighbourhood learning and gender mutation, the discrete eel swarm intelligence algorithm is proposed for combinatorial optimization problems. Finally, numerical experiments on the TSP problem and the permutation flow-shop scheduling problem show that the algorithm has strong optimization capability and accuracy.

关键词： swarm intelligence algorithm optimization problem concentration adaptation neighborhood learning gender mutation

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Research on swarm intelligence optimization algorithm

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The Journal of China Universities of Posts and Telecommunications 2020年第3期27卷 1-20,30页

作者： Fei Wei Liu Cong Hu Sheng Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan 430068China Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy Hubei University of Technologywuhan 430068China Wuhan Hua'an Science and Technology Co. Ltd.Postdoctoral WorkstationWuhan 430068.China

The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization *** simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent *** paper first introduces the principles of various swarm intelligent optimization ***,the typical application of these swarm intelligence optimization algorithms in various fields is *** that,the advantages and defects of all swarm intelligence optimization algorithms are ***,the improvement strategies of various swarm intelligence optimization algorithms are ***,the future development of various swarm intelligence optimization algorithms is prospected.

关键词： bionics natural heuristic algorithm swarm intelligence algorithm intelligent computing

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ELA:A New swarm intelligence algorithm

ELA:A New Swarm Intelligence Algorithm

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2014 IEEE International Conference on Progress in Informatics and Computing

作者： Yaosheng Sun Zhangcan Huang Yu Chen School of Science Wuhan University of Technology

By analyzing the living behaviors of eels, this paper proposes a new eel swarm intelligence algorithm. This paper first describes the behavior of migratory eels, extracts three important behaviors--density adaption, neighboring learning and sex mutation, and establishes a model for the mathematical description of the three important behaviors. Based on rational organization of the three important behaviors, the eel algorithm is designed for continuous optimization problems. Finally, we test the performance of eel swarm intelligence algorithm via several selected benchmark problems. The results show that the algorithm, in terms of its excellent convergence speed and solving accuracy, is competitive tothe compared algorithms.

关键词： heuristic search algorithms continuous algorithm optimization problem swarm intelligence algorithm migration behavior

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Efficient maximum iterations for swarm intelligence algorithms: a comparative study

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ARTIFICIAL intelligence REVIEW 2025年第3期58卷 1-49页

作者： Si-Ma, Shen Liu, Han-Ming Zhan, Hong-Xiang Liu, Zhao-Fa Guo, Gang Yu, Cong Hu, Peng-Cheng Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China Gannan Normal Univ Jiangxi Educ Inst Key Lab Data Sci Artif Intelligence Ganzhou 341000 Peoples R China Ganzhou Teachers Coll Ganzhou 341000 Peoples R China

A swarm intelligence algorithm usually iterates many times to approximate the optimum to obtain the solution of a problem. The maximum iteration is influenced by many factors such as the algorithm itself, problem types, as well as dimensions and search space sizes of decision variables. There are few existing studies on efficient maximum iterations, especially a large-scale study on comparison for different problem types. By dividing three CEC benchmark sets into several problem types, this study made a large-scale performance comparison of 123 common swarm intelligence algorithms from several views. The experimental results show that for low-dimensionality, wide search space, and/or simple- and medium-complex problems, about a quarter of the algorithms are concentrated in iterations of about 30 similar to 80, while most algorithms for other types of problems tend to have as many iterations as possible. By and large, for the Classical set, large iterations are beneficial for improving the performance of most algorithms, while less than half of the algorithms for CEC 2019 and CEC 2022 do so. And, the efficient iterations of excellent algorithms are about 300 on low dimensionality, wide search space and simple-complexity problems, while other types are as large as possible. In terms of algorithm speed, LSO, DE and RSA are the fastest on all the three benchmark sets, and the runtime of all algorithms is almost linearly related to the maximum iterations. Although the conclusions largely depend on the problem types, we believe that an efficient iteration is necessary to optimize algorithm performance.

关键词： swarm intelligence algorithm Efficient maximum iteration Large-scale comparison CEC benchmark set

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Hybrid swarm intelligence and Human-Inspired Optimization for Urban Drone Path Planning

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BIOMIMETICS 2025年第3期10卷 180-180页

作者： Ji, Yidao Liu, Qiqi Zhou, Cheng Han, Zhiji Wu, Wei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Beihang Univ Inst Unmanned Syst Beijing 100191 Peoples R China Ocean Univ China Coll Engn Qingdao 266404 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments.

关键词： urban drone path planning swarm intelligence algorithm human-inspired algorithm

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