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  1. 581

    Energy storage configuration considering user-shared costs in peak shaving auxiliary services with improved multi-objective particle swarm optimization by Yiyou Xing, Jin Shen, Xinru Li

    Published 2025-04-01
    “…Moreover, an improved particle swarm optimization algorithm, specifically adapted for this model, is developed. …”
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    Article
  2. 582

    Low-carbon optimization planning method for integrated energy system based on DG uncertainty affine model by JIANG Tao, XU Cong, JIA Shaohui, WANG Shen, ZHANG Yajian

    Published 2024-08-01
    Subjects: “…ladder carbon trading;integrated energy system;affine model;differential evolution particle swarm algorithm;interval optimization…”
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    Article
  3. 583

    Fault localization for automatic train operation based on the adaptive error locating array algorithm by Yanpeng Zhang, Yuxiang Cao

    Published 2025-01-01
    “…These cases are designed to locate the MFS more easily in the given parameter range using the Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, the MFS is determined. …”
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  4. 584

    Kinematics Analysis and Trajectory Optimization of the Hybrid Welding Robot by Cai Ganwei, Ban Caixia, Tian Junwei, Zhang Kechen

    Published 2023-12-01
    “…At the same time, a trapezoidal function with parabolic transition is used to plan the end effector of the hybrid welding robot, and the multi-objective trajectory optimization scheme is proposed. The particle swarm optimization algorithm is used to optimize the time parameters, and the optimal time parameters that meet the positioning accuracy of the end of the welding machine manipulator are solved. …”
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    Optimization of ATIG Weld Based on a Swarm Intelligence Approach: Application to the Design of Welding in Selected Manufacturing Processes by Kamel Touileb, Sahbi Boubaker

    Published 2025-05-01
    “…Based on the numerical observations, linear and nonlinear models for describing the effect of the thermophysical parameters on the weld characteristics were tuned using a particle swarm optimization algorithm. While the linear model provided good prediction accuracy, the nonlinear exponential model outperformed the linear one for the depth yielding a mean absolute percentage error of 17%, a coefficient of determination of 0.8266, and a root mean square error of 0.9665 mm. …”
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