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A distributed PSOSVM hybrid system with feature selection and parameter optimization
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This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the clas
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粒子群算法在离散问题中的应用。这里主要是在TSP问题中的应用。-Discrete Particle Swarm Optimization Problems. Here the main problem in the application of TSP.
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离散微粒群优化算法解决流水线调度问题。包含多个有名算法：NEH，局部搜索算法等。-Discrete Particle Swarm Optimization algorithm to solve assembly line scheduling problem. Contains several well-known algorithms: NEH, local search algorithm.
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二进制粒子群的源代码，可以用来给初学者作为学习的模板。只要修改目标函数，就可以运行解决离散的优化问题-Binary particle swarm of source code, and can be used as a template for beginners to learn. As long as the objective function changes can run to solve the problem of the optimization of the discrete
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神经网络的经典应用，小波神经网络、离散神经网络、模糊神经网络、遗传算法、粒子群优化-Classic application of neural networks, wavelet neural network, discrete neural networks, fuzzy neural networks, genetic algorithms, particle swarm optimization
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In this paper, a novel Discrete Particle Swarm Optimization Algorithm
(DPSOA) for data clustering has been proposed. The particle positions and velocities
are defined in a discrete form. The DPSOA algorithm uses of a simple probability
approach
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simplified swarm optimization，用來改善particle swarm optimization在離散型問題的優化演算法。內容僅包含最簡單之演算模式，可自行運用在求解的問題上。-simplified swarm optimization, to improve particle swarm optimization problems in discrete optimization algorithms. Contains only the most simple al
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改进后的离散粒子群算法，支持多种寻优解决方案-The improved discrete particle swarm optimization (pso), support for multiple optimal solutions
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fcm-svm模型，用来处理连续型数据的拟合预测建模，想用SOM算法将连续性数据离散化，paosvm为改进的粒子群优化SVM参数的算法。数据可根据自己的进行替换-fcm-svm model fit to handle continuous data, predictive modeling, would like to use SOM algorithm continuity discrete data, paosvm of improved particle swarm optimizatio
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本MATLAB程序是基于离散粒子群算法的可中断负荷的优化计算-The MATLAB program is a discrete particle swarm optimization algorithm based on interruptible load calculation
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离散粒子群算法。粒子群算法原来只能处理连续变量，算法进行了改进，可以处理离散变量-Discrete particle swarm optimization algorithm, particle swarm optimization algorithm can only deal with continuous variables, the algorithm is improved, can handle discrete variables
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离散二进制粒子群算法，介绍了编码方式，实现了离散问题的寻优，有借鉴意义-Binary particle swarm algorithm, introduced coding, enabling a discrete optimization problem, there is reference
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变邻域，含变惯性权重策略的自适应离散粒子群算法，完美运行，适应函数是多式联运路径优化实例计算-Variable Neighborhood, Adaptive Discrete Particle Swarm Variable inertia weight strategy perfectly, fitness function is multimodal path optimization calculation examples
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验证基于DPSO 算法的合作感知调度方案在各个LN 的网络特征不同
的异构CRNMLNS 中也能获得同样的性能(CRNMLNS, a cooperative sensing scheduling scheme based on discrete particle swarm optimization (DPSO) algorithm was proposed)
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Bat algorithm, Discrete Fourier transform proposed by Pie, Fourier Mellin Transform, Particle swarm optimization algorithm
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离散粒子群算法，改进的二值粒子群算法，可以正常运行(Discrete particle swarm optimization algorithm, improved binary particle swarm optimization algorithm)
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