自适应插值飞蛾扑火优化的多特征粒子滤波车辆跟踪算法
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黄鹤, 吴琨, 李昕芮, 王珺, 王会峰, 茹锋
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A Multi-Feature Particle Filter Vehicle Tracking Algorithm Based on Adaptive Interpolation Moth-Flame Optimization
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HUANG He, WU Kun, LI Xinrui, WANG Jun, WANG Huifeng, RU Feng
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表1 5种算法在测试函数.上的实验对比
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Tab.1 Experimental comparison of five algorithms in test functions
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测试函数 | 算法 | 最优值 | 均值 | 标准差 | 测试函数 | 算法 | 最优值 | 均值 | 标准差 | Sphere | AWPSO | 0.79 | 2.89 | 2.01 | Ackley | AWPSO | 1.58 | 3.41 | 2.11 | | MFO | 1.28×10-14 | 3.18×10-14 | 4.97×10-14 | | MFO | 1.84×10-8 | 3.58×10-7 | 3.75×10-7 | | IMFO | 2.61×10-19 | 1.44×10-15 | 3.62×10-15 | | IMFO | 2.49×10-10 | 2.82×10-8 | 4.61×10-8 | | AMFO | 8.16×10-44 | 4.44×10-39 | 3.94×10-39 | | AMFO | 8.88×10-16 | 2.01×10-14 | 5.61×10-14 | | AIMFO | 1.23×10-48 | 5.46×10-44 | 7.12×10-44 | | AIMFO | 8.88×10-16 | 8.88×10-16 | 8.88×10-16 | Schwefel’s 2.22 | AWPSO | 0.56 | 2.01 | 0.69 | Penalized1 | AWPSO | 1.03 | 3.98 | 2.61 | | MFO | 8.66×10-10 | 5.21×10-9 | 3.82×10-9 | | MFO | 6.45×10-16 | 2.59×10-12 | 5.15×10-11 | | IMFO | 1.31×10-12 | 1.82×10-10 | 2.99×10-10 | | IMFO | 5.35×10-16 | 5.52×10-15 | 7.45×10-15 | | AMFO | 5.16×10-24 | 8.41×10-23 | 9.51×10-23 | | AMFO | 4.83×10-16 | 2.53×10-15 | 1.32×10-15 | | AIMFO | 2.13×10-28 | 5.05×10-27 | 6.48×10-27 | | AIMFO | 2.12×10-17 | 8.11×10-16 | 1.21×10-16 |
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