基于混合蛙跳算法的齿轮传动优化设计 by 何兵, 车林仙, 刘初升

“…The hybrid optimization algorithm（HODEFL） overcame the disadvantages on low precision and premature convergence of shuffled frog leaping algorithm（SFLA） for high-dimensional optimization by taking advantages of strong global search and rapid convergence of DE/best/2/bin（DEb2） in differential evolution algorithm（SDE）.The SFLA and DE are hybridized to form a hybrid optimization algorithm（HODEFL） in order to overcome the disadvantages of the SFLA.The study object is the optimization design of the cylindrical helical gear reducer,establishing minimum volume.By comparing with the improved particle swarm optimization（LWPSO）,SFLA and with DEb2 evolutionary algorithm,the HODEFL algorithm is superior to other three algorithms in terms of optimization efficiency,computational accuracy and robustness.…”
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A cooperative jamming resource allocation method based on PSO-SSNOA by Wei Liu, You Chen, Xiaohong Zhao, Dejiang Lu, Tianjian Yang

“…Subsequently, we propose a Particle Swarm Optimization Guided Seasonal Strategy Nutcracker Optimization Algorithm (PSO-SSNOA) based jamming resource allocation method. …”
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Improved sequence-based localization applied in coal mine by Mingzhi Song, Jiansheng Qian

“…Sequence–centroid localization contributes to improving this issue, but the location error on the boundary of whole area is unsatisfactory as well. This article proposes an improved sequence-based localization method which is integrated with quantum-behaved particle swarm optimization, as quantum-behaved particle swarm optimization makes good use of the search performance of global optimal solution. …”
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Prediction of Transformer Residual Flux Based on J-A Hysteresis Theory by Qi Long, Xu Yang, Keru Jiang, Changhong Zhang, Mingchun Hou, Yu Xin, Dehua Xiong, Xiongying Duan

“…Firstly, an improved particle-swarm optimization algorithm is proposed in this paper to address the problem of slow convergence speed and susceptibility to local optima in current particle-swarm optimization algorithms for extracting J-A model parameters. …”
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Neuro-evolutionary models for imbalanced classification problems by Israa Al-Badarneh, Maria Habib, Ibrahim Aljarah, Hossam Faris

“…The utilized algorithms are the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Salp Swarm Algorithm (SSA). …”
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Reliability Prediction for Computer Numerical Control Machine Servo Systems Based on an IPSO-Based RBF Neural Network by Zheng Jiang, GuangJian Wang, ZuGuang Huang, Ye He, RuiJuan Xue

“…The major influences on the reliability of servo system include torque, temperature, current, and complexity. An improved algorithm for predicting the mean time between failure (MTBF) of servo systems based on a particle swarm optimization (PSO) and an RBF neural network algorithm is proposed. …”
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Economic Dispatch Using Parameter-Setting-Free Harmony Search by Zong Woo Geem

“…Economic dispatch is one of the popular energy system optimization problems. Recently, it has been solved by various phenomenon-mimicking metaheuristic algorithms such as genetic algorithm, tabu search, evolutionary programming, particle swarm optimization, harmony search, honey bee mating optimization, and firefly algorithm. …”
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Cooperative Path Planning for Multi-UAVs with Time-Varying Communication and Energy Consumption Constraints by Jia Guo, Minggang Gan, Kang Hu

“…In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). …”
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Comparative Analysis of Bio-Inspired Enhancement Techniques for Localization in 2D Wireless Sensor Networks by Rabhi Seddik

“…This study conducts a comparative evaluation of three bio-inspired optimization algorithms for node localization: Particle Swarm Optimization (PSO), Fruit Fly Optimization Algorithm (FOA), and Drop Mongoose Optimization Algorithm (DMOA). …”
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Construction of teaching quality evaluation model of online dance teaching course based on improved PSO-BPNN by Jin Ben, Li Hanwen

“…For these shortcomings, it is optimized by the improved particle swarm. This study uses inertia weight and crossover operator strategy to optimize particle swarm optimization algorithm, achieving the collaborative work of back-propagation neural network and particle swarm optimization. …”
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Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM by A. Tianjian Yang, B. Xing Wang, C. Siyi Cheng, D. You Chen, E. Xi Zhang

“…For the problem of evaluating the effectiveness of cooperative jamming to group network radar by formation aircraft, a cooperative jamming effectiveness evaluation method based on Improved Particle Swarm Optimization–Extreme Learning Machine (IPSO-ELM) is proposed. …”
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Extracting Pole Characteristics of Complex Radar Targets for the Aircraft in Resonance Region Using RMSPSO_ARMA by Sang Zhou, Huotao Gao, Fangyu Ren

“…The paper proposes a new method to extract the characteristic resonance set of radar targets in the resonance frequency region using an autoregressive moving average model optimized by root mean square propagation particle swarm optimization. …”
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Load Frequency Control of an Isolated Power System in Presence of Controllable Energy Storage Devices by Mallareddy Mounica, Chinta Prasad, D. J. V Prasad, M. Aruna Bharathi

“…In each case the effect of different storage units are studied for random load changes. Here particle swarm optimization algorithm (PSO) and improved particle swarm optimization technique (IPSO) are used to tune the parameters of storage units to provide proper control strategy for the system. …”
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Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory by Tianyi Wang, Wenjun Yi, Youran Xia

“…The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. …”
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Low-Injury Rubber Tapping Robots: A Novel PSO-PID Approach for Adaptive Depth Control in <i>Hevea Brasiliensis</i> by Ruiwu Xu, Yulan Liao, Junxiao Liu, Zhifu Zhang, Xirui Zhang

“…Nevertheless, natural rubber tapping robots encounter considerable challenges in achieving precise tapping, particularly in controlling tapping depth, due to the lack of suitable control algorithms. To solve this problem, an improved Particle Swarm Optimization/Proportional–Integral–Derivative (PSO-PID) control method has been proposed in this paper. …”
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铰链四杆刚体导引机构综合的区间逃逸粒子群算法 by 易建, 何兵, 车林仙

“…The length of the bars is regarded as the restrict condition to obtain the unconstrained optimization model for rigid-body guidance approximate kinematc synthesis of hinged 4-bar linkages and this optimal problem is solved by means of the particle swarm optimization （PSO） algorithm. …”
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Multi-UAVs task allocation method based on MPSO-SA-DQN by Peng Pengfei, Gong Xue, Zheng Yalian

“…Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. …”
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Real-time active cell balancing using QPSO-controlled switched capacitor and transformer methods by S. Ida Evangeline, B. Subashini

“…To address these limitations, this paper proposes a novel hybrid active balancing approach that integrates switched capacitor and transformer-based techniques, dynamically controlled by a quantum particle swarm optimization algorithm. The hybrid system combines the speed of switched capacitor balancing for localized voltage differences with the long-range capabilities of transformer-based balancing, enabling efficient energy redistribution across large battery packs. …”
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Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions by Jianbo Bai, Leihou Sun, Rupendra Kumar Pachauri, Guangqing Wang

“…Its main idea is to determine the initial position of particles and remove the acceleration factor and random number in traditional particle swarm optimization (PSO) algorithm. …”
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Research on Railway Passenger Volume Forecast Based on the Spline Interpolation and IPSO-Gradient Difference Acceleration Rule by Dingyuan Fan, Fei Yang, Jinghao Ji, Zexi Zhang

“…The research results show that the spline interpolation method has a better prediction effect after processing abnormal passenger traffic data, and the improved particle swarm algorithm also shows better optimization ability and convergence speed when solving the double difference postulate. …”
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