论文研究核心目标是评估改进动态多种群粒子群算法(DMPG)在无人机路径规划中的应用效果,特别是在动态环境下的性能表现。该算法通过引入动态权重调整机制和多种群策略,旨在增强算法在动态环境下的适应性和优化性能。此外,本文还结合了...论文研究核心目标是评估改进动态多种群粒子群算法(DMPG)在无人机路径规划中的应用效果,特别是在动态环境下的性能表现。该算法通过引入动态权重调整机制和多种群策略,旨在增强算法在动态环境下的适应性和优化性能。此外,本文还结合了深度强化学习(DRL)技术,以提高无人机在复杂环境中的自主决策能力。通过构建详细的仿真环境,我们对DMPG算法进行了全面的性能评估,包括其避障能力、路径规划的效率以及对动态变化的响应速度。仿真结果显示,DMPG算法在动态环境中表现出色,不仅能够有效规避障碍物,而且在路径规划的全局性和鲁棒性方面均取得了显著提升。与现有的静态和动态路径规划算法进行比较,DMPG算法在平均路径长度、避障成功率以及任务完成时间等关键性能指标上均展现出了卓越的性能。这些发现为无人机路径规划的研究提供了新的见解,并为未来相关领域的研究和应用提供了有价值的参考。The core objective of this paper is to evaluate the application effect of improved Dynamic Multi Swarm Particle Swarm Optimization (DMPG) algorithm in UAV path planning, especially its performance in dynamic environments. This algorithm aims to enhance its adaptability and optimization performance in dynamic environments by introducing dynamic weight adjustment mechanisms and various swarm strategies. In addition, this article also combines deep reinforcement learning (DRL) technology to improve the autonomous decision-making ability of drones in complex environments. By constructing a detailed simulation environment, we conducted a comprehensive performance evaluation of the DMPG algorithm, including its obstacle avoidance ability, path planning efficiency, and response speed to dynamic changes. The simulation results show that the DMPG algorithm performs well in dynamic environments, not only effectively avoiding obstacles, but also achieving significant improvements in the global and robust aspects of path planning. Compared with existing static and dynamic path planning algorithms, the DMPG algorithm has demonstrated excellent performance in key performance indicators such as average path length, obstacle avoidance success rate, and task completion time. These findings provide new insights for the study of drone path planning and valuable references for future research and applications in related fields.展开更多
荷电状态SOC(state of charge)是锂离子电池的重要参数之一,SOC的精准估计对电池组安全可靠运行具有重要意义。针对误差反向传播BP(back propagation)神经网络易收敛至局部最优,导致基于BP网络的SOC估计精度不高的问题,提出子种群自适...荷电状态SOC(state of charge)是锂离子电池的重要参数之一,SOC的精准估计对电池组安全可靠运行具有重要意义。针对误差反向传播BP(back propagation)神经网络易收敛至局部最优,导致基于BP网络的SOC估计精度不高的问题,提出子种群自适应趋同策略改进思维进化算法,用其优化BP神经网络的初始权值及阈值,优化后的BP网络简称SAMEA-BP神经网络。结合充放电实验数据,将SAMEA-BP神经网络与标准BP神经网络、思维进化算法优化的BP(MEA-BP)神经网络用于锂离子电池的SOC估计,并对3种方法做了对比分析。结果表明:标准BP神经网络的预测误差保持在9%以内,MEA-BP及SAMEA-BP神经网络分别将误差降低至5%及3%以内,在不同工况下和不同温度下,SAMEA-BP有良好适应性,且估计精度高于BP和MEA-BP。展开更多
文摘论文研究核心目标是评估改进动态多种群粒子群算法(DMPG)在无人机路径规划中的应用效果,特别是在动态环境下的性能表现。该算法通过引入动态权重调整机制和多种群策略,旨在增强算法在动态环境下的适应性和优化性能。此外,本文还结合了深度强化学习(DRL)技术,以提高无人机在复杂环境中的自主决策能力。通过构建详细的仿真环境,我们对DMPG算法进行了全面的性能评估,包括其避障能力、路径规划的效率以及对动态变化的响应速度。仿真结果显示,DMPG算法在动态环境中表现出色,不仅能够有效规避障碍物,而且在路径规划的全局性和鲁棒性方面均取得了显著提升。与现有的静态和动态路径规划算法进行比较,DMPG算法在平均路径长度、避障成功率以及任务完成时间等关键性能指标上均展现出了卓越的性能。这些发现为无人机路径规划的研究提供了新的见解,并为未来相关领域的研究和应用提供了有价值的参考。The core objective of this paper is to evaluate the application effect of improved Dynamic Multi Swarm Particle Swarm Optimization (DMPG) algorithm in UAV path planning, especially its performance in dynamic environments. This algorithm aims to enhance its adaptability and optimization performance in dynamic environments by introducing dynamic weight adjustment mechanisms and various swarm strategies. In addition, this article also combines deep reinforcement learning (DRL) technology to improve the autonomous decision-making ability of drones in complex environments. By constructing a detailed simulation environment, we conducted a comprehensive performance evaluation of the DMPG algorithm, including its obstacle avoidance ability, path planning efficiency, and response speed to dynamic changes. The simulation results show that the DMPG algorithm performs well in dynamic environments, not only effectively avoiding obstacles, but also achieving significant improvements in the global and robust aspects of path planning. Compared with existing static and dynamic path planning algorithms, the DMPG algorithm has demonstrated excellent performance in key performance indicators such as average path length, obstacle avoidance success rate, and task completion time. These findings provide new insights for the study of drone path planning and valuable references for future research and applications in related fields.
文摘荷电状态SOC(state of charge)是锂离子电池的重要参数之一,SOC的精准估计对电池组安全可靠运行具有重要意义。针对误差反向传播BP(back propagation)神经网络易收敛至局部最优,导致基于BP网络的SOC估计精度不高的问题,提出子种群自适应趋同策略改进思维进化算法,用其优化BP神经网络的初始权值及阈值,优化后的BP网络简称SAMEA-BP神经网络。结合充放电实验数据,将SAMEA-BP神经网络与标准BP神经网络、思维进化算法优化的BP(MEA-BP)神经网络用于锂离子电池的SOC估计,并对3种方法做了对比分析。结果表明:标准BP神经网络的预测误差保持在9%以内,MEA-BP及SAMEA-BP神经网络分别将误差降低至5%及3%以内,在不同工况下和不同温度下,SAMEA-BP有良好适应性,且估计精度高于BP和MEA-BP。
