搜索资源列表
pso
- 实现粒子群算法，里面有代码，大家可以放心的使用，里面程序是可以用的 。-fgfggfg
gene_regulatory
- modelling gene regulatory network with hybridized DE and PSO
10.1.1.76.1148
- Variable Neighborhood PSO Algorithm
RLSexample
- Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and several papers on this topic [5][7][13][15], one of which did a s
single-objective-minimization
- single objective optimization using pso document is present
Standard_PSO_2006_m
- Standard PSO with C-Standard PSO with C++
PSO--Overview_v2
- L’apparition des algorithmes évolutionistes à fait l’effet d’une bombe dans les domaines de la résolution de problèmes complexes, et spécialement dans l’optimisation de fonction avec contraintes. L’optimisation par essaim de particules se présent
Adptve-BF-techniques
- This document is about research area of noise reduction techniques. Here the research area is in adaptive beam forming techniques
PSO_meander-line
- PSO optimization. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving.
MOGA
- A multi-objective PSO for job-shop scheduling problems
PSOgongjuxiang
- pso工具介绍，pso Tools ，pso Tools-pso Tools
1
- During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but
PSo
- 基本的粒子群算法程序，很好的程序 ，用matlab编写的-The basic particle swarm optimization procedure, a good program, written using matlab
SVD-based-watermarking
- 基于SVD的DCT域和DWT域的经典水印算法，该算法通过提取水印水印的主成分并把该主成分嵌入到奇异值矩阵，鲁邦性非常高。-the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT) and for the second method, those are embedded into the host image in discrete
pso
- powepoint for partical swarm optimization
test
- PSO,PSO standard benchmarks Test
partha_func
- function used for pso
initialization
- initialization file for pso
1234255
- 介绍了一种利用量子行为粒子群算法(QPSO)求解多峰函数优化问题的方法。为此，在 QPSO中引进一种物种形成策略，该方法根据群体微粒的相似度并行地分成子群体。每个子群体是 围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索。从而保证每个峰值都有 同等机会被找到，因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算 法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明，基于物 种形成的QPSO算法可以尽
5346363636
- ：针对粒子群算法进行多极点函数优化时 存在的局部极小点和搜寻效率低的问题，引入了小 生境的思想到粒子群算法中，以粒子的最好位置为 中心，粒子的最好的个体解对应的适应值为半径建 立圆形小生境。stretching 技术，其次对子群体采用解散策略，即当在子群体中找到一个极值点后把子群体解散回归主群体，最 后设置子群体创建时的半径阈值，避免子群体半径过大。该算法解决了标准的NichePS0算法在处理 多峰函数时，极值点的个数依赖于子群体个数及极值点容易出现重复、遗漏