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多阈值图像分类的粒子群优化算法，采用自然方法来提高优化速度和计算量-Multi-threshold image classification of the particle swarm optimization algorithm, using natural methods to improve the optimization speed and computation
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利用粒子群算法做的关于基于指数熵对数熵TSALLIS熵的多阈值的图像分割，内附无优化算法，可以做时间比较-Using particle swarm optimization to do on the entropy-based index of the number of entropy TSALLIS entropy of multi-threshold image segmentation, containing no optimization, can do more time
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最大类间方差法是图像分割中一种常用的阈值分割方法, 对于单阈值分割具有显著的效果, 但是对于
多阈值分割, 计算复杂度大、耗时较多。本文将粒子群优化算法与最大类间方差法结合, 提出了一种新的图像分
割方法, 该方法利用粒子群优化算法的寻优高效性, 并由灰度图像的最大类间方差值作为适应值, 搜索最优分割
阈值, 实现图像的多阈值分割。实验结果显示, 新方法大大缩短了寻找最优阈值的时间, 降低了运算复杂度, 提
高了图像分割速度, 说明基于粒子群优化算法的图像分割算法是可行的、有效的
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粒子群滤波器实现多目标追踪的程序，本方法是基于Durrant-Whyte的算法-Particle Swarm filter multi-target tracking procedures, the method is based on the algorithm of Durrant-Whyte
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改进的多目标粒子群算法，包括多个测试函数
对程序中的部分参数进行修改将更好地求解某些函数-Improved multi-objective particle swarm algorithm, modify some parameters of the program including a plurality of test functions will be better for solving certain functions
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The particle swarm optimization (PSO) algorithm is a new population based search strat-
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer prem
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0下载：
The particle swarm optimization (PSO) algorithm is a new population based search strat-
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer prem
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