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multi-objective-
- 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
program
- 编写了一个pso优化bp神经网络的程序，应用在分类中。第一步：pso优化bp神经网络得到最优的阈值和权值，第二步bp神经网络把该最优的阈值和权值作为初始阈值和权值，采用动量及自适应学习速率算法进行训练。附件中，是数据和编写的部分程序，tiqushuju是用来提取文本中的数据构造样本集的函数。mubiao是用来构造期望输出的函数。bp是已经编写好的，未使用pso优化的bp神经网络函数。pso是本人编写的pso优化bp神经网络的函数，psobp是采用pso优化的阈值和权值作为bp神经网络的初始权值和
PLS_tracker_tip
- Matlab下的实时跟踪程序，内附demo程序，也可以选择手动选择跟踪区域，这种方式需要将demo中的一段代码前的注释去掉-Main file: Tracking_PLS.m You can set the initial position of the target object with know parameters or select the target region manually in the first frame. You can tune the \Si
Qpso_test
- 量子粒子群算法源码（对应须文波、孙俊等人的论文）带测试函数-Quantum particle swarm algorithm source code (corresponding Wenbo, SUN Jun et al paper) with test function
newpso
- 这是一种改进的粒子群优化算法，通过简单的测试函数验证-This is an improved particle swarm optimization, through a simple test function verification
differential-grouping
- 利用差分算法，实现了自动高维度分解的粒子群算法，适合大规模协同进化的自动差分，分解问题规模，是粒子群算法的优化算法，包含测试函数、测试数据。-Using finite difference algorithm, and realized automatic decomposition of high dimension of particle swarm optimization (pso) algorithm, is suitable for large-scale cooperative c
PSO
- 这里是个简单的粒子群优化（PSO）算法的源程序，大家可以直接测试，关于人脸识别的一个程序，有需要的可以借鉴-Here is a simple particle swarm optimization (PSO) algorithm source code, we can directly test a program on face recognition, there is need to learn
cmopso
- 典型的多目标粒子群算法（CMOPSO)，附测试函数,注解详细，适合学习参考-A typical multi-objective particle swarm optimization (CMOPSO), attached to the test function, annotation detailed reference for learning
acpsomatlabchengxu
- 自适应混沌粒子群算法的matlab程序，有测试函数-Adaptive chaotic particle swarm algorithm matlab procedures, test function
APIT_CS-2003-06
- 传统的近似三角形内点测试( APIT),即近似三角形内点测试定位算法,广泛应用于静态节点定位.结合粒子滤波提出改进算法,将APIT算法推广到节点动态定位.-Traditional approximate point inside the triangle test (APIT), namely the approximate point within the triangle test positioning algorithm, widely used in the static node l
PSO
- it is the matlab code of particle swarm optimization with a test function that can be change-it is the matlab code of particle swarm optimization with a test function that can be change..
PSOGSA_v3
- This a Hybrid PSO GSA Aigorithm.A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation
pso-1
- 给出了经典的粒子群优化算法的matlab实现方法，并结合测试函数，显示算法的寻优能力-Gives a classical particle swarm optimization algorithm matlab implementation, combined with the test function, the ability to display optimization algorithm
LIZIQUNotsu
- 采用协 同和带压缩因子的粒子群改进算法求解Ot su 阈值, 通过分别用改进粒子群算法和标准粒子群算法对lena 测试图像 的实验表明, 前者相较于后者有更高的精度-With the use of synergies and improved compression factor of the particle swarm algorithm Ot su threshold, respectively, by using the improved PSO and PSO standard
CMOPSO3
- Coello Coello等人提出了MOPSO。该matlab源程序针对test function 3的matlab程序。该算法引入了自适应网格机制的外部种群，不仅对群体的粒子进行变异，而且对粒子的取值范围也进行变异，且变异尺度与种群进化的代数成比例。-reference:Handling Multiple Objectives With Particle Swarm Optimization Carlos A. Coello Coello, Member, IEEE, Gregorio
CMOPSO4
- Coello Coello等人提出了MOPSO。该程序针对test function4的matlab程序。该算法引入了自适应网格机制的外部种群，不仅对群体的粒子进行变异，而且对粒子的取值范围也进行变异，且变异尺度与种群进化的代数成比例。-reference:Handling Multiple Objectives With Particle Swarm Optimization Carlos A. Coello Coello, Member, IEEE, Gregorio Toscano P
CMOPSO5
- Coello Coello等人提出了MOPSO。该程序针对test function 5的matlab程序。该算法引入了自适应网格机制的外部种群，不仅对群体的粒子进行变异，而且对粒子的取值范围也进行变异，且变异尺度与种群进化的代数成比例。-reference:Handling Multiple Objectives With Particle Swarm Optimization Writer:Carlos A. Coello Coello, Member, IEEE, Gregorio Tos
duomubiaoliziqunsuanfa
- 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, including multiple test functions of some parameters on the program will be modified to better solve certain functions
fpa_demo
- Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the
PSOGSA
- 程序提出了一种基于人群混合算法，是粒子群优化和引力搜索算法的组合。主要目标提高整合PSO和GSA算法的能力-Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization