资源列表
多目标粒子群算法
- 利用粒子群算法求解函数的极值，用于多目标模型优化(Solving the extreme value of a function using particle swarm optimization algorithm for multi-objective model optimization)
三重迁移学习代码和数据
- 三重迁移学习代码以及数据，用于发现文本分类~~(Triple migration learning code and data for discovering shared and unique concepts in text categorization~~)
带交叉算子的量子粒子群优化算法
- 带交叉算子的量子粒子群优化算法~~~~~~~~~~(Quantum Particle Swarm Optimization with Crossover Operator)
QACO
- 量子蚁群优化算法，用于求解复杂优化问题~(Quantum ant colony optimization algorithm for solving complex optimization problems~)
适用量子粒子群算法
- 适用量子粒子群算法获得所需的最优值，可以依概率收敛到全局最优，能有效求解复杂优化问题(Quantum Particle Swarm Optimization (QPSO) can be applied to obtain the desired optimal value, which can converge to the global optimum according to probability, and can effectively solve complex optimizatio
量子行为的粒子群算法-SVM
- 改进量子粒子群算法，用于优化支持向量机参数，用IRIS数据验证(An improved quantum particle swarm optimization (QPSO) algorithm is used to optimize the parameters of support vector machine (SVM), which is validated by IRIS data.)
动态蜂群算法源程序
- 动态蜂群算法源程序，实现复杂优化的求解~~~(Dynamic swarm algorithm source program to achieve complex optimization solution ~~~)
模拟退火算法
- MATLAB模拟退火算法，包含两个程序，一个是优化一元函数的，一个是优化多元函数的(MATLAB simulated annealing algorithm, including two programs, one is to optimize the unary function, one is to optimize the multivariate function)
Lipschitz指数和模极大值
- 本程序主要用于求解信号小波变换后，信号奇异点处的李氏指数和模极大值，对信号的断点进行定量的描述，可用于信号故障诊断。(This program is mainly used to solve the Lie index and the modulus maxima at the signal singular point after signal wavelet transform, and quantitatively describe the breakpoint of the signal
FDDL
- 基于Fisher字典学习的稀疏表示分类算法。(Sparse representation classification algorithm based on Fisher dictionary learning.)
models.geomech.triaxial_test
- 岩石试件三轴压缩试验的数值模拟模型，可以得到应力应变曲线图。文件为建模步骤，模型较大，无法上传，需要者可联系。(The stress-strain curve can be obtained by the numerical simulation model of triaxial compression test of rock specimens. The file is a modeling step. The model is large and can not be uploaded.
VRPTW-ga
- 带时间窗的车辆路径问题求解的python代码(VRP with time window(python))