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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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Research on the optimum synchronous network search data extraction based on swarm intelligence algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 125卷 151-155页

作者： Hu, Su Yin, Hua Guangdong Univ Finance & Econ Sch Informat Sci Guangzhou 510320 Guangdong Peoples R China

Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic balancing model is constructed to determine the node balance state according to the traffic variation and influence factors of network nodes. Under the condition that the node state is known, the swarm intelligence algorithm is used to cluster the data to be synchronized and adjust the node state so that it is kept stable throughout the synchronization process. The clustered data act as the target to connect the user with the server side to achieve the optimum network search data extraction and synchronization. The experimental results show that when the number of concurrent network users is increasing, the designed technique features stable load balancing, and achieves optimum data extraction performance and low execution cost when the task completion time is less than 0.5 s. (C) 2021 Elsevier B.V. All rights reserved.

关键词： swarm intelligence algorithm Network search Data extraction Synchronous update

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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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Welding sequence optimization to reduce welding distortion based on coupled artificial neural network and swarm intelligence algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2022年第0期114卷

作者： Wu, Chunbiao Wang, Chao Kim, Jae-Woong Changshu Inst Technol Sch Mech Engn Changshu 215500 Jiangsu Peoples R China Yeungnam Univ Sch Mech Engn Gyongsan 38541 Gyeongbuk South Korea

This study aims to develop a welding sequence optimization (WSO) framework based on coupled artificial neural network (ANN) and swarm intelligence algorithm for minimizing welding distortion of thin-walled squared Al-Mg-Si alloy tube components. This framework is mainly composed of two critical computer programs. Firstly, a multilayer feedforward backpropagation neural network (BPNN) system was established to rapidly estimate residual distortion for an arbitrary welding sequence so that welding sequence can be optimized for achieving desired welding quality. For this purpose, a series of nonlinear thermo-elastic-plastic finite element (FE) simulations were conducted and verified with experiments to generate the input database of the neural network. Subsequently, a reliable BPNN model was successfully created and trained within an acceptable error. Secondly, a novel swarm intelligence algorithm, namely, bees algorithm (BA) was proposed to solve the complicated WSO problems. In this optimization process, the trained BPNN model was implanted into this proposed BA for computing the fitness value of arbitrary welding sequences. Moreover, welding experiments were also performed to confirm the performance of the proposed optimization method. Comparing the results from experimental measurements, FE simulations, and proposed WSO framework, it is demonstrated that this proposed BPNN-and-BA-based WSO framework can be successfully applied in practical engineering to obtain an optimal welding sequence for minimizing final welding distortion.

关键词： Welding sequence optimization (WSO) Artificial neural network (ANN) Backpropagation neural network (BPNN) swarm intelligence algorithm Bees algorithm (BA)

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

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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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Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2020年第2期23卷 663-671页

作者： Milan, Sara Tabagchi Rajabion, Lila Darwesh, Aso Hosseinzadeh, Mehdi Navimipour, Nima Jafari Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Univ South Florida Sarasota Manatee Coll Business Sarasota FL USA Univ Human Dev Informat Technol Dept Sulaymaniyah Iraq Iran Univ Med Sci Hlth Management & Econ Res Ctr Tehran Iran Univ Human Dev Comp Sci Sulaymaniyah Iraq Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr Iran

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm.

关键词： Cloud computing Scheduling algorithms Energy swarm intelligence algorithm

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Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm

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INFORMATION SCIENCES 2020年 536卷 25-52页

作者： Lu, Hui Liu, Yaxian Cheng, Shi Shi, Yuhui Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

Parameter control is critical for the performance of any swarm intelligence algorithm. In this study, we propose an adaptive online data-driven closed-loop parameter control (CLPC) strategy for a swarm intelligence algorithm to solve both single-objective and multi-objective optimization problems with better performance. The proposed CLPC strategy involves three key parts: controller design, feedback selection, and reference determination. First, based on the control theory, we adopt a proportional integral derivative (PID) controller in the CLPC strategy, which can adaptively adjust the value of parameter according to the difference between reference and feedback. Second, to reflect and monitor the evolution state in real time, we use the mean shift clustering method and define the convergence entropy and the extension entropy to generate feedback. Finally, the reference should provide useful guidance for parameter control according to the features of optimization problems. Thus, in single-objective optimization, we propose a new lossless fitness landscape analysis method and design a decision tree to determine the reference;in multi-objective optimization, the range of the convergence entropy and the extension entropy are regarded as the reference. In addition, to illustrate the effectiveness of the CLPC strategy, two groups of experiments are performed based on the particle swarm optimization (PSO) algorithm in single-objective and multi-objective optimization. At the optimization algorithm level, we compare our proposed CLPC-PSO algorithm with three standard PSO algorithms, three decreasing inertia weight PSO algorithms, and two adaptive PSO algorithms. At the optimization problem level, we perform abundant experiments based on five single-objective benchmark functions, five multi-objective benchmark functions, and twelve scheduling instances. The statistical results show that the performance of the proposed CLPC-PSO algorithm is considerably better and more sta

关键词： swarm intelligence algorithm Closed-loop parameter control strategy PID control system Population clustering Convergence and extension entropy

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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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A swarm intelligence algorithm for Routing Recovery Strategy in Wireless Sensor Networks With Mobile Sink

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IEEE ACCESS 2018年 6卷 67434-67445页

作者： Yue, Yinggao Cao, Li Hang, Bo Luo, Zhongqiang Hubei Univ Arts & Sci Comp Sch Xiangyang 441053 Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov Yibin 644000 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China

Considering the fault tolerance mechanism in the route optimization of the mobile wireless sensor network (MWSN), we analyze the routing fault tolerance between nodes and establish an intelligent fault-tolerant routing model for MWSN. We also propose a novel fault-tolerant routing algorithm for an MWSN based on an artificial bee colony (ABC) optimized particle swarm optimization algorithm (ABC-PSO), and this optimizes the ABC-PSO algorithm is applied to study the optimal construction strategy of an alternate route. The proposed using of the path coding, the ABC algorithm optimization, the collaborative updation, and the evolution of the principal and subordinate swarms, as well as particle selection, provide faster overall convergence performance and more accurate solutions for the network optimization. Analytical proofs and simulation experiments show that the route fault-tolerant strategy proposed in this paper can create a reliable transmission environment and an efficient route recovery mechanism for the MWSN. Moreover, it can lower the energy consumption of the network and increase the network's lifetime and improve the robustness and the reliability of MWSN.

关键词： Mobile wireless sensor networks fault tolerant swarm intelligence algorithm particle swarm optimization algorithm reliability

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