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particle swarm optimization algorithm based on teaming behavior

KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Yu, Yu-Feng Wang, Ziwei Chen, Xinjia Feng, Qiying Guangzhou Univ Dept Stat Guangzhou 510006 Peoples R China Guangzhou Univ Sch Cyberspace Secur Guangzhou 510006 Peoples R China

The traditional particle swarm optimization algorithms have some shortcomings, such as low convergence precision, slow convergence speed, and susceptibility to falling into local optima when solving complex optimization problems. To address these issues, this paper proposes a new particle swarm optimization algorithm that incorporates teamwork. Specifically, we introduce the concept of teamwork, and divide the particles into multiple teams and selecting team leaders. The particles can fully utilize the team's prompt information to guide the search process. The team leader updates the search direction of its particles through the generation of information factors, thus giving the algorithm better global search capabilities. The position and behavior of the team leader affect the search behavior of other particles, reducing the risk of falling into local optimal solutions. In addition, to further improve the algorithm's efficiency, we propose adaptive adjustment of information factors and learning factors. This adaptive adjustment mechanism enables the algorithm to adjust parameters flexibly according to the characteristics of the problem and the current search state, thereby accelerating convergence speed and improving convergence precision. To verify the performance of the proposed algorithm, we make an empirical analysis on 27 different test functions, the shortest path problem and the optimal SINR value problem for UAV deployment. The experimental results show that the proposed algorithm has obvious advantages in convergence accuracy and convergence speed. Compared with other algorithms, this algorithm can find a better solution faster and converge to the global optimal solution more stably.

关键词： Teaming behavior Information factor Self-adaptive modification particle swarm optimization algorithm

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particle swarm optimization algorithm can Promote Cooperation in Public Goods Game with Costly Punishment 39

Particle Swarm Optimization Algorithm can Promote Cooperatio...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhao, Xiaowei Liu, Guangrui Xu, Xiujuan Ilan, Xu Dalian Inst Technol Sch Software Dalian Peoples R China Liaoning Normal Univ Sch Management Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Previous researches have well demonstrated the importance of costly punishment for promoting the evolution of cooperation in spatial public goods games, while it remains unconsidered sides about the role of strategy-updating mechanisms. Inspired by the algorithms in the field of Machine Learning, in this paper, we propose a game strategy updating mechanism based on particle swarm optimization algorithm for spatial public goods game with continuous strategies, and explore the impact of tolerance-based punishment mechanisms on the evolution of cooperation. The results of simulation experiments show that the particle swarm optimization algorithm can effectively promote cooperation under appropriate parameter settings. This result reveals that hybrid learning is more conducive to maintaining cooperation than a single learning mechanism (social learning or self-learning), and can prevent the spread of betrayal and maintain a high level of cooperation when betrayal strategies invade.

关键词： public goods game particle swarm optimization algorithm spatial reciprocity evolution of cooperation

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Orthodontic path planning for virtual teeth via the multi-strategy improved particle swarm optimization algorithm

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-28页

作者： Li, Hong-an Hu, Xue Zhao, Zhihua Liu, Jun Xian Univ Sci & Technol Coll Comp Sci & Technol Xian 710054 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Shanxi Conservancy Tech Inst Dept Informat & Engn Yuncheng 044000 Peoples R China Shaanxi Tech Coll Finance Econ Xianyang 712099 Peoples R China

Orthodontic path planning is a critical dental problem that directly affects the orthodontic outcome and patient experience. To improve the accuracy and efficiency of orthodontics, this paper proposes an orthodontic path planning method that combines an improved particle swarm optimization algorithm with a collision avoidance movement prioritization strategy. First, the efficiency of orthodontic path planning is improved by designing a local coordinate system based on the direction of tooth growth and the direction of neighboring teeth to reduce manual intervention. Second, a multi-strategy improved particle swarm optimization algorithm is proposed for orthodontic path planning, where the population is initialized by cosine sequence mapping interference linear interpolation, and the particles are adaptively updated using linear inertia weights and trigonometric function factors. An annealing-PSO strategy and particle stochastic learning strategy are also introduced to enhance the ability of the algorithm to jump out of the local optimum. In addition, a collision avoidance movement prioritization strategy based on low orthodontic costs and OBBTree is proposed to detect and avoid collisions between teeth effectively. Finally, through experimental validation on nine benchmark functions and a set of orthodontic cases involving both maxillary and mandibular regions, the MSIPSO algorithm demonstrated a reduction of 31.43% in maxillary orthodontic translation and 10.03% in rotation compared to the traditional PSO algorithm. Furthermore, comparisons with other optimization algorithms, including NSMPSO, CSPSO, and PSO-SA, further highlight the superior performance of the MSIPSO algorithm in terms of convergence speed and optimization accuracy. The results show that the method can effectively plan high-quality orthodontic paths, which can be used as a reference for medical aid diagnosis.

关键词： Orthodontic Tooth movement prioritization particle swarm optimization algorithm OBBTree Simulated annealing algorithm

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Adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update

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swarm AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Wang, Liangying Hong, Lihuan Fu, Haoxuan Cai, Zhiling Zhong, Yiwen Wang, Lijin Fujian Agr & Forestry Univ Coll Comp & Informat Fuzhou 350002 Fujian Peoples R China Fujian Prov Univ Key Lab Smart Agr & Forestry Fuzhou 350002 Fujian Peoples R China Wuyi Univ Fujian Key Lab Big Data Applicat & Intellectualiza Wuyishan 354300 Fujian Peoples R China

In recent years, multi-objective particle swarm optimization algorithms have been widely used in science and engineering due to their advantages of fast convergence speed and easy implementation. However, the selection of globally optimal particle is an important and challenging problem in the design of multi-objective particle swarm optimization algorithms. In this regard, this paper proposes an adaptive distance-based multi-objective particle swarm optimization algorithm with simple position update, named ADMOPSO. First, an adaptive penalty-based boundary intersection (PBI) distance strategy is designed to select the globally optimal particle from two elite particles which are randomly chosen from an elite particle set. This strategy better balances the diversity and convergence requirements of particle swarm optimization algorithm in the optimization process. Second, a simple position probabilistic update strategy is constructed to rewrite the velocity update method with the weight and use the learning rate to control the scale of the updated velocity in the position update equation to avoid particle swarm falling into the local optimum. Finally, an extensive experimental study is conducted to test the performance of several selected multi-objective optimization algorithms on ZDT, WFG and DTLZ benchmark problems, as well as 7 real-world problems were conducted to test the proposed algorithm. Comparative experimental results show that the algorithm proposed in this paper has significant advantages over other algorithms. This shows that the ADMOPSO algorithm is competitive in dealing with multi-objective problems.

关键词： Competitive mechanism particle swarm optimization algorithm Multi-objective optimization Adaptive distance Position update

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A composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution for global optimization problems and engineering problems

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ADVANCES IN ENGINEERING SOFTWARE 2025年 202卷

作者： Hao, Rui Hu, Zhongbo Xiong, Wentao Jiang, Shaojie Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China Yangtze Univ Jingzhou Hosp Jingzhou Hubei Peoples R China

particle swarm optimization (PSO) and its numerous performance-enhancing variants area kind of stochastic optimization technique based on collaborative sharing of swarm information. Many variants took current particles and historical particles as current and historical information to improve their performance. If future information after each current swarm can be mined to participate in collaborative search, the algorithmic performance could benefit from the comprehensiveness of the information including historical, current and future information. This paper proposes a composite particle swarm optimization algorithm with future information inspired by non-equidistant grey predictive evolution, namely NeGPPSO. The proposed algorithm firstly employs non-equidistant grey predictive evolution algorithm to predict a future particle as future information for each particle of a current swarm. Secondly, four particles including prediction particle, particle best and swarm best of the current swarm, and a history memory particle are used as guide particles to generate four candidate positions. Finally, the best one in the four positions is greedily selected as an offspring particle. Numerical experiments are conducted on 42 benchmark functions given by the Congress on Evolutionary Computation 2014/2022 and 3 engineering problems. The experimental results demonstrate the overall advantages of the proposed NeGPPSO over several state-of-art algorithms.

关键词： Future information Improved particle swarm optimization algorithm Non-equidistant grey prediction evolutionary particle swarm optimization algorithm

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Design a robust intelligent power controller for pressurized water reactor using particle swarm optimization algorithm

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KERNTECHNIK 2024年第5期89卷 690-698页

作者： Ateya, Afaf A. E. Saeed, Rehab M. Kandil, Magy M. Egyptian Atom Energy Author EAEA Cairo Egypt

Proportional-Integral-Derivative (PID) controllers have been optimized and used to overcome many types of problems in nuclear reactor systems. The high performance of PID controllers depend on optimizing their gains. In this research, an optimized robust PID controller is proposed to control power perturbations in a pressurized water reactor (PWR). The optimization process of robust PID using particle swarm optimization (PSO) algorithm aims to adapt PID gains then after that, H-infinity controller is used. The results show a good performance when that suggested hybrid controller is applied to the nuclear power system since the suggested design makes the system robust due to applying H-infinity method, in addition to get the benefits of the optimized PID controller.

关键词： pressurized water reactor proportional-integral-derivative controllers robust H-infinity controller particle swarm optimization algorithm

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Underwater image processing and target detection from particle swarm optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-14页

作者： Zhang, Yangmei Bi, Yang Li, Junfang Xian Aeronaut Inst Sch Elect Engn Xian 710077 Peoples R China

The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. I

关键词： Visual saliency analysis Emerging multimedia Underwater image processing particle swarm optimization algorithm Itti model Target tracking

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Enhancing Continuum Robotics Accuracy Using a particle swarm optimization algorithm and Closed-Loop Wire Transmission Model for Minimally Invasive Thyroid Surgery

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APPLIED SCIENCES-BASEL 2025年第4期15卷 2170-2170页

作者： Guo, Na Zhang, Haoyun Li, Xingshuai Cui, Xinnan Liu, Yang Pan, Jiachen Song, Yajuan Zhang, Qinjian Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Tsinghua Univ Inst Precis Med Beijing 100084 Peoples R China Univ Southern Calif Phys Dept Los Angeles CA 90007 USA Beijing Informat Sci & Technol Univ Sch Mech & Elect Engn Beijing 100192 Peoples R China Univ Sci & Technol Beijing Sch Adv Engn Beijing 100083 Peoples R China

To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid-continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the particle swarm optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master-slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability.

关键词： continuum robots particle swarm optimization algorithm closed-loop wire transmission model minimally invasive thyroid surgery

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Parameters identification of magnetorheological damper based on particle swarm optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 143卷

作者： Guo, Qianqian Yang, Xiaolong Li, Kangjun Li, Decai Guangxi Univ Sci & Technol Sch Mech & Automot Engn Liuzhou 545006 Guangxi Peoples R China Tsinghua Univ State Key Lab Tribol Beijing 100084 Peoples R China

The parameter setting of the optimization algorithm is of significant importance in establishing a mechanical model with high accuracy. This study employs a combination of experimental and numerical methods to comprehensively examine the impact of optimization algorithm parameters on the accuracy of fitting results. The objective is to provide technical support for the precise prediction of the damping force in the control of the suspension system, as well as the optimization of vehicle driving performance. This paper employs the most prevalent particle swarm optimization algorithm and meticulously examines the impact of alterations in parameters, including the number of particles, the number of iterations and the learning factors, on the identification outcomes. The experimental data pertaining to the magnetorheological damper is obtained through investigation, and the parameters of the magnetorheological damper are identified through the utilisation of a numerical research methodology, specifically the particle swarm optimization algorithm. Finally, the veracity of the identified results is validated through a comparison of the identified damping force with the experimental damping force, thereby illustrating the significance of optimizing the algorithm parameter settings in enhancing the precision of the mechanical model.

关键词： Magnetorheological damper Parameter identification particle swarm optimization algorithm

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Behavioral Modeling of SiC MOSFET Static and Dynamic Characteristics Based on particle swarm optimization algorithm

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IEEE ACCESS 2025年 13卷 66994-67005页

作者： Zhu, Zhibo Zhao, Yang Yan, Wei Nanjing Vocat Univ Ind Technol Coll Elect Engn Nanjing 210023 Peoples R China Nanjing Normal Univ Jiangsu Engn Key Lab Elect Equipment & Electromagn Nanjing 210042 Peoples R China

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have been widely applied in electronic equipment, owing to the rapidly switching speed and superior thermal performance. Under high-frequency switching, the parasitic parameters in MOSFET driving circuit and power circuit will cause problems such as voltage overshoot and oscillation, which limit the application of power devices. To improve the stability and reliability of electronic circuit, considering that the operating frequency of the MOSFET reaches several hundred kHz, an accurate high-frequency model of the SiC MOSFET should be established, including static and dynamic models. This study proposes a SiC MOSFET behavioral model with parasitic parameters based on the particle swarm optimization (PSO) algorithm. The PSO algorithm is used to extract the parameters of the improved Enz-Krummnacher-Vittoz model and a static characteristic behavioral model of the SiC MOSFET is established, avoiding a large amount of simulation or calculation time for circuit and datasheet-drive models, the efficiency is increased by 60%, and the model accuracy is 98% compared to the datasheet. On the basis of the static characteristics, a nonlinear capacitance model is established using the PSO algorithm, and the parasitic inductance of the MOSFET is extracted through a finite element analysis. The fourth-order Runge-Kutta method is employed to solve the state equation, and the dynamic characteristic behavioral model of the MOSFET is established based on the static characteristics, parasitic capacitance and inductance, device and circuit parameters, which is simpler and easier to implement than the physical modeling method. A pulse testing experimental circuit is constructed to validate the accuracy of the dynamic model, compared with the sample model and experimental results, the errors of the behavioral model are less than 3%. This study provides valuable insights for MOSFET modeling and optimization,

关键词： Semiconductor device modeling MOSFET Integrated circuit modeling Silicon carbide Mathematical models Analytical models Accuracy Heuristic algorithms Logic gates SPICE Behavioral modeling dynamic characteristic nonlinear capacitance particle swarm optimization algorithm SiC MOSFET static characteristic

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