te

算法预测与特征重要性分析

ret:
<class ‘tuple’>: (
[5, 0, 27, 111, 179, 102, 25, 30, 35, 210],

[0.8058252427184466, 0.8155774395702775, 0.8355314197051978, 0.8328530259365994, 0.8365384615384616, 0.8371313672922251, 0.841897233201581, 0.8332239001969797, 0.8321358589157414, 0.8358306188925081],

[0.5251752708731676, 0.7119184193753983, 0.8215423836838751, 0.8846398980242193, 0.9279796048438496, 0.9509241555130656, 0.9674952198852772, 0.9706819630337795, 0.975780752071383, 0.9783301465901848])

ret_imp:
<class ‘tuple’>: (
[9, 2, 0, 7, 77, 185, 111, 65, 49, 64],

[0.9214092140921409, 0.9412780656303973, 0.9245810055865922, 0.9315403422982885, 0.9356913183279743, 0.931615460852329, 0.9270142180094787, 0.9283088235294118, 0.9283185840707965, 0.9242685025817556],

[0.23518164435946462, 0.3690248565965583, 0.45634161886551944, 0.521351179094965, 0.5946462715105163, 0.6430847673677501, 0.6724028043339707, 0.6934353091140854, 0.7202039515615042, 0.7405991077119184])

z:
5:<class ‘list’>: [5, 7] [0.5640771895101435, 0.9523809523809523] [0.5118, 0.5118]

0: <class ‘list’>: [5, 8] [0.4397854705021941, 1.0] [0.2068, 0.1872]

27: <class ‘list’>: [10, 3] [0.4596322941646683, 1.0] [0.6333, 0.1792]

111: <class ‘list’>: [7, 5] [0.5189274447949527, 1.0] [0.2812, 0.0759]

179:<class ‘list’>: [5, 7, 9] [0.439581351094196, 0.723404255319149, 1.0] [0.0797, 0.0645, 0.0624]

102:<class ‘list’>: [8, 5] [0.440279860069965, 1.0] [0.8819, 0.0983]

25 : <class ‘list’>: [8] [0.9805194805194806] [0.0631]

30 : <class ‘list’>: [1, 0] [0.5989847715736041, 1.0] [0.6589, 0.1936]

35: <class ‘list’>: [8, 0] [0.440279860069965, 1.0] [0.8819, 0.1819]

210: <class ‘list’>: [8, 4] [0.5604395604395604, 1.0] [0.8819, 0.15]

imp:

9:<class ‘dict’>: {‘feature’: [3, 8], ‘coverage’: [0.2193877551020408], ‘precision’: [0.9156976744186046]}

2: <class ‘dict’>: {‘feature’: [3, 2], ‘coverage’: [0.14094387755102042], ‘precision’: [0.9864253393665159]}

0: <class ‘dict’>: {‘feature’: [3, 8], ‘coverage’: [0.09566326530612244], ‘precision’: [0.8733333333333333]}

7: <class ‘dict’>: {‘feature’: [3, 4], ‘coverage’: [0.11734693877551021], ‘precision’: [0.9836956521739131]}

77: <class ‘dict’>: {‘feature’: [3, 4], ‘coverage’: [0.12053571428571429], ‘precision’: [0.9629629629629629]}

185: <class ‘dict’>: {‘feature’: [3, 4], ‘coverage’: [0.07334183673469388], ‘precision’: [0.8956521739130435]}

111: <class ‘dict’>: {‘feature’: [3, 4], ‘coverage’: [0.03635204081632653], ‘precision’: [0.8245614035087719]}

65 : <class ‘dict’>: {‘feature’: [3, 8], ‘coverage’: [0.022321428571428572], ‘precision’: [0.9142857142857143]}

49 : <class ‘dict’>: {‘feature’: [3, 4], ‘coverage’: [0.02295918367346939], ‘precision’: [0.8333333333333334]}

64: <class ‘dict’>: {‘feature’: [3, 8], ‘coverage’: [0.021045918367346938], ‘precision’: [1.0]}

内容概要:本文介绍了一个基于冠豪猪优化算法(CPO)的无人机三维路径规划项目,利用Python实现了在复杂三维环境中为无人机规划安全、高效、低能耗飞行路径的完整解决方案。项目涵盖空间环境建模、无人机动力学约束、路径编码、多目标代价函数设计以及CPO算法的核心实现。通过体素网格建模、动态障碍物处理、路径平滑技术和多约束融合机制,系统能够在高维、密集障碍环境下快速搜索出满足飞行可行性、安全性与能效最优的路径,并支持在线重规划以适应动态环境变化。文中还提供了关键模块的代码示例,包括环境建模、路径评估和CPO优化流程。; 适合人群:具备一定Python编程基础和优化算法基础知识,从事无人机、智能机器人、路径规划或智能优化算法研究的相关科研人员与工程技术人员,尤其适合研究生及有一定工作经验的研发工程师。; 使用场景及目标:①应用于复杂三维环境下的无人机自主导航与避障;②研究智能优化算法(如CPO)在路径规划中的实际部署与性能优化;③实现多目标(路径最短、能耗最低、安全性最高)耦合条件下的工程化路径求解;④构建可扩展的智能无人系统决策框架。; 阅读建议:建议结合文中模型架构与代码示例进行实践运行,重点关注目标函数设计、CPO算法改进策略与约束处理机制,宜在仿真环境中测试不同场景以深入理解算法行为与系统鲁棒性。
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