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swarm intelligence algorithm-Based Optimal Design of Microwave Microfluidic Sensors

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第2期69卷 2077-2087页

作者： Zhao, Wen-Sheng Wang, Bin-Xiao Wang, Da-Wei You, Bin Liu, Qi Wang, Gaofeng Hangzhou Dianzi Univ MOE Engn Res Ctr Smart Microsensors & Microsyst Sch Elect & Informat Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat MOE Key Lab RF Circuits & Syst Hangzhou 310018 Peoples R China

Microwave microfluidic sensors have been employed for dielectric characterization of different liquids. Intuitively, the microfluidic channel plays a vital role in determining the sensor performance. In this article, for the first time, numerical optimization design of microfluidic channel route is carried out with the aim of improving the sensor sensitivity. Two swarm intelligence algorithms, i.e., particle-ant colony optimization algorithm and wolf colony algorithm, are implemented for the route optimization. Through the developed optimization procedure, the sensor sensitivity of the original design can be increased significantly. Several prototypes of optimized sensors are fabricated and tested, and they exhibit good capability in retrieving the liquid properties. In comparison with original complementary split-ring resonator-based sensor with a sensitivity of 0.308% for water measurement, the optimized sensor achieves a high sensitivity value of 0.55%, i.e., the sensor sensitivity is increased by 78.6% after optimization. The developed methodology can also be used in other designs, such as series LC-based sensor, whose sensitivity can be improved by about 50%. It is demonstrated that the developed methodology possesses good automatic optimization ability and universality for the optimal design of microwave microfluidic sensors.

关键词： Sensors Microfluidics Optimization Sensitivity Intelligent sensors Liquids Sensor phenomena and characterization Microfluidic channel particle-ant colony optimization (PACO) relative permittivity sensor sensitivity swarm intelligence algorithm wolf colony algorithm (WCA)

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Entertainment robots based on swarm intelligence algorithm applied in remote dance performances

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Chen, Nan Guizhou Univ Finance & Econ Sch Art Guiyang 550025 Peoples R China

With the continuous development of technology, the application of entertainment robots in the field of dance performances is receiving increasing attention. This article focuses on the application of entertainment robots based on swarm intelligence algorithms in remote dance performances, aiming to design an intelligent dance performance control system to achieve precise and smooth dance performances of robots. The study introduces the basic artificial fish swarm algorithm and combines it with mobile robot path planning, proposing a mobile robot path planning method based on swarm intelligence algorithm. By simulating the behavior of fish in schools, the optimization of path planning is achieved. The control system process of the entertainment robot intelligent dance performance control system includes preparation work before the dance performance, data transmission and dance posture control during the performance process, and post performance organization work. The experiment verified the effectiveness and reliability of the intelligent dance performance control system for entertainment robots. The experimental results show that the system can achieve precise, smooth, and diverse dance performances, providing a novel and interesting form of dance performance for the audience.

关键词： swarm intelligence algorithm Entertainment robots Dance performance Remote control

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Performance analysis of geometrically shaped 16/32/64/128QAM based on swarm intelligence algorithm

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OPTICAL FIBER TECHNOLOGY 2025年 90卷

作者： Lu, Jia Wang, Tianshuo Ma, Jie Liu, Jianfei Zeng, Xiangye Wang, Yang Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China Hebei Prov Key Lab Adv Laser Technol & Equipment Tianjin 300401 Peoples R China

In this paper, an optimization scheme for geometrically shaped quadrature amplitude modulation (GS-QAM) based on swarm intelligence algorithms is proposed. The swarm intelligence algorithms of the marine predator algorithm (MPA), nonlinear marine predator algorithm (NMPA), mountain gazelle optimizer (MGO), dog optimization algorithm (DOA), and honey badger algorithm (HBA) are used to optimize the geometrical locations of the constellation points to reduce the damage caused by phase noise and improve the system performance. The results show that the scheme improves the optical signal-to-noise ratio (OSNR) gain by 1.4 dB/1.6 dB/2.8 dB/4.3 dB, compared with the standard 16/32/64/128 QAM signals and the optimization effect becomes more obvious as the modulation order increases. The five algorithms also significantly improve the performance of the system in terms of transmission distance and transmission rate. In addition, the scheme further validates the universality of the proposed optimization scheme for different modulation formats and demonstrates its potential application to higher-order signals.

关键词： swarm intelligence algorithm Optical signal-to-noise ratio Geometrically shaped quadrature amplitude modulation Coherent optical communication system

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Design and Research of Accounting Automation Management System Based on swarm intelligence algorithm and Deep Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 996-1005页

作者： Gui, Dan Ma, Wei Chen, Wanfei State Grid Henan Elect Power Co Informat & Commun Financial Assets Dept Zhengzhou 450000 Peoples R China State Grid Henan Elect Power Co Finance Dept Zhengzhou 450000 Peoples R China

the current research, the application verification of traditional algorithms in actual accounting management is insufficient, and deep learning data processing capabilities need to be fully optimized in complex accounting scenarios. Given the challenges of efficiency and accuracy faced by the current accounting industry in the context of big data, this study creatively combines the swarm intelligence algorithm and deep learning technology to design and implement an efficient and accurate accounting automation management system. The research aims to investigate the potential of swarm intelligence algorithms and deep learning techniques in developing an automated accounting management system, with a focus on improving efficiency, accuracy, and scalability. Key research questions include exploring the optimal configuration of swarm intelligence algorithms for accounting tasks and assessing the performance of deep learning models in automating various accounting processes. Through experimental verification, the system is tested with the financial data of a large enterprise for three consecutive years. The results show that the system can significantly shorten the time of financial statement generation by 65%, reduce the error rate to less than 0.5%, and increase the accuracy of abnormal data recognition by as much as 90%. These data not only reflect the significant improvement of the efficiency and accuracy of the system but also prove its great potential in early warning of financial risk, providing intelligent and automated solutions for the accounting industry.

关键词： swarm intelligence algorithm deep learning accounting automation management

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Design of robust fractional order fuzzy PID sliding mode controller based on hybrid swarm intelligence algorithm for a 6-DOF robotic manipulator

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ROBOTICA 2025年

作者： Karahan, Oguzhan Karci, Hasan Kocaeli Univ Dept Elect & Commun Engn Kocaeli Turkiye

In this paper, fractional-order (FO), intelligent, and robust sliding mode control (SMC) and stabilization of inherently nonlinear, multi-input, multi-output 6-DOF robot manipulators are investigated. To ensure robust control and better performance of the robot system, significant studies on various control transactions have been explored. First, a sliding proportional-integral-derivative (PID) surface is conceived and then its FO constitute is developed. It is an important fact that in SMC, the reaching phase is fast and the chattering is abated in the sliding phase. In particular, the discontinuity in the SMC is prevented in view of the boundary layer obtained by recommending the sigmoid function together with fuzzy logic to eliminate the chattering phenomenon. A hybrid tuning method consisting of gray wolf optimization and particle swarm optimization (GWO-PSO) algorithms is applied to tune the parameters of PID sliding mode control (PIDSMC), FO PIDSMC (FOPIDSMC), fuzzy PIDSMC (FPIDSMC), and FO fuzzy PIDSMC (FOFPIDSMC) controllers. In simulation results, the tuned FOFPIDSMC controller consistently outperforms PIDSMC, FOPIDSMC, and FPIDSMC controllers tuned by the GWO-PSO in dynamic performance, trajectory tracking, disturbance rejection, and mass uncertainty scenarios. It has been seen through a thorough performance analysis that 91.93% and 44.13% improvement are, respectively, obtained for mean absolute error (MAE) and torques root mean square (RMS) values of the joints when using from the PIDSMC to the FOFPIDSMC. Finally, the simulation outcomes reveal the superior aspects of the designed FOFPIDSMC and also demonstrate that the FOFPIDSMC controller enhances the dynamic performances of the 6-revolute universal robots 5 (6R UR5) robot manipulator under a variety of operating conditions.

关键词： 6-DOF robotic manipulator swarm intelligence algorithm sliding mode control fuzzy logic fractional order

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swarm intelligence algorithm for extracting spatial spectrum features of hyperspectral remote sensing image and decomposing mixed pixels

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5045-5055页

作者： Gao, Yu Pan, Yinsong Huang, Hong Mohamed, Ehab R. Aly, Zahraa M., I Chongqing Univ City Coll Sci & Technol Sch Elect Informat Chongqing 402167 Peoples R China Chongqing Univ Coll Optoelect Engn Chongqing Peoples R China Zagazig Univ Fac Comp & Informat Zagazig Egypt

Hyperspectral remote sensing combines spectrum, ground space and images organically to provide humans with unprecedented rich information. However, a prominent problem faced in the extraction and identification of hyperspectral remote sensing information is mixed pixels, and the method to solve mixed pixels is mixed pixel decomposition. The purpose of this paper is to study the swarm intelligence algorithm of spatial-spectral feature extraction and mixed pixel decomposition of hyperspectral remote sensing images. This paper first introduces two different methods for extracting spatial spectrum features, then studies linear and non-linear spectral hybrid models, and then studies end element extraction methods based on quantum particle swarm optimization. The degree inversion method, the experimental part is based on the accuracy of the quantum particle swarm optimization-based end-element extraction method and two spatial-spectrum feature extraction methods. The experimental results show that the algorithm proposed in this paper improves the effect of group pixel decomposition based on the swarm intelligence algorithm. The classification accuracy of the 3DLBP spatial spectrum feature proposed in this paper is 94.22%.

关键词： Hyperspectral remote sensing image spatial spectral feature extraction mixed pixel decomposition swarm intelligence algorithm abundance inversion

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Intelligent Scheduling Technology of swarm intelligence algorithm for Drone Path Planning

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DRONES 2024年第4期8卷 120-120页

作者： Meng, Zhipeng Li, Dongze Zhang, Yong Yan, Haoquan Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Nanjing Univ Aeronaut & Astronaut Res Inst Pilotless Aircraft Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Peoples R China

Different kinds of swarm intelligence algorithm obtain superior performances in solving complex optimization problems and have been widely used in path planning of drones. Due to their own characteristics, the optimization results may vary greatly in different dynamic environments. In this paper, a scheduling technology for swarm intelligence algorithms based on deep Q-learning is proposed to intelligently select algorithms to realize 3D path planning. It builds a unique path point database and two basic principles are proposed to guide model training. Path planning and network learning are separated by the proposed separation principle and the optimal selection principle ensures convergence of the model. Aiming at the problem of reward sparsity, the comprehensive cost of each path point in the whole track sequence is regarded as a dynamic reward. Through the investigation of dynamic environment conditions such as different distances and threats, the effectiveness of the proposed method is validated.

关键词： algorithm scheduling deep reinforcement learning path planning swarm intelligence algorithm

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Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

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JOURNAL OF BUILDING ENGINEERING 2024年 98卷

作者： Tian, Haozheng Zhang, Yunsheng Qiao, Hongxia Feng, Qiong Wang, Penghui Lanzhou Univ Technol Sch Civil Engn Lanzhou 730050 Peoples R China Lanzhou Univ Technol Western Minist Civil Engn Disaster Prevent & Mitig Lanzhou 730050 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Guangdong Prov Key Lab Durabil Marine Civil Engn Shenzhen 518060 Peoples R China

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6-14-2. Simultaneously, three swarm intelligence algorithms were integrated-the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)-to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 x 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT-BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 an

关键词： Polymer-modified mortar swarm intelligence algorithm Mechanical properties BAT-BPNN Manufactured sand

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Optimization of artificial CNN based on swarm intelligence algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 6163-6173页

作者： Li, Qian Li, Shuyuan Changchun Guang Hua Univ Sch Elect & Informat Engn Changchun Peoples R China

Aiming at the precocious convergence, low search accuracy and easy divergence of most particle swarm optimizations with velocity terms, a particle swarm optimization (IWPSO) with random inertia weights and quantization is proposed. First, the inertia weights are obeyed to be distributed randomly, and the learning factors are adjusted asynchronously to optimize the parameters in BP network. Secondly, BP network is trained using the IWPSO algorithm based on the sample data. Finally, simulation experiments prove that the algorithm has significantly improved search speed, convergence accuracy, and stability compared with existing improved algorithms. Due to the characteristics of IWPSO algorithm, the BP neural network optimized by IWPSO has better global convergence performance and is an efficient particle swarm optimization.

关键词： Optimization artificial neural network swarm intelligence algorithm

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A novel dual-biological-community swarm intelligence algorithm with a commensal evolution strategy for multimodal problems

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JOURNAL OF SUPERCOMPUTING 2021年第10期77卷 10850-10895页

作者： Ren, Hui Shen, Xiaochen Jia, Xiaojun Commun Univ China State Key Lab Media Convergence Commun Sch Informat & Commun Engn 1 Dingfuzhuang East St Beijing 100024 Peoples R China

For sophisticated applications, engineers should always consider multi-objective, multi-task or multi-modal problems, especially in the Internet of Things, such as the data fusion of multi-sensor systems, multiple routing problems for automation and convergence control in wireless sensor networks. Normally, utilizing traditional methods costs abundant resources and yields the homogeneous results that fails to satisfy requirements. For the multimodal situation, this paper proposes the dual-biological-community swarm intelligence algorithm based on particle swarm optimization (DBC-PSO), which is combined with the commensal evolution strategy to enhance the convergence ability. This algorithm can split tasks into two communities and guarantee regional changes in information and search accuracy for multimodal problems through a commensal strategy. Moreover, some typical parameters, including the population rate, velocity and radius influence, are considered to study the algorithm performance. The performance is evaluated on 12 well-known multimodal problems, and the simulation is compared with some algorithms that utilize a similar evolutionary approach. The results indicate that the proposed algorithm exhibits strong performance and is very promising for use in more productive work.

关键词： Multimodal problems swarm intelligence algorithm Internet of Things (IoT) Commensal evolution strategy

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