搜索资源列表
PSO_Java
- 同遗传算法比较,PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化,神经网络训练,模糊系统控制以及其他遗传算法的应用领域,基于Java语言实现。-Compared with the genetic algorithm, PSO has the advantage is simple and easy and there is no need to adjust many parameters. Has been widely applied to function o
psoandga
- 粒子群算法及其与遗传算法的比较,加深交流!-pso ga compare
Ackely
- Ackley函数的粒子群优化算法的实现,对初学粒子群算法的有用-Ackley function of the particle swarm optimization algorithm, particle swarm optimization for beginners useful
PSO
- 智能优化算法,有粒子群算法(matlab)和多岛遗传算法(C语言)-PSO optimization algorithm and nsga-II optimization algorithm
GA-a-PSOPBP
- 遗传算法及粒子群算法优化的BP神经网络,用于多输入多输出的神经网络预测模型-GA and PSO optimized BP neural network. Can be used for ANN prediction models with multiple inputs and outputs
Particle-algorithm
- 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation),有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究。 PSO同遗传算法类似,是一种基于叠代的优化工具。系统初始化为一组随机解,通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。 同遗传算法比较,PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化,神经网络训练
PSO
- 数学建模Pso算法 模拟退火 遗传算法-Pso mathematical modeling algorithm simulated annealing genetic algorithm, etc.
GA-to-RBF
- GA to RBF 神经网络,遗传算法,滤波器,MATLAB-GA to RBF neural networks, genetic algorithms, filters, MATLAB
pso-simulation
- 粒子群优化算法的源程序代码,其中结合了遗传算法进行优化。-Particle swarm optimization algorithm source code, which combines the genetic algorithm optimization.
GA-PSO
- 遗传算法结合粒子群算法的例子,采用matlab编程-Particle swarm optimization with genetic algorithms example, using matlab programming
standard-pso-and-improved
- 一个遗传算法的总结,包括标准的遗传算法和几个改进的遗传算法。 -A genetic algorithm is summarized, including the standard genetic algorithm and several the improved genetic algorithm.
csa-ga-pso
- 人工免疫算法、粒子群算法、遗传算法、寻优的matlab程序 -Artificial immune algorithm, particle swarm optimization, genetic algorithms, optimization matlab program
A-new-approach-of-PSO-GA
- 基于PSO及遗传算法的改进,用于并行性方面的研究-PSO and Genetic Algorithm for the parallelism
pso
- pso 算法 matlab 粒子群算法 遗传算法
PSO-matlab-general-coding-
- 遗传算法matlab程序,PSO matlab coding for general using-PSO matlab coding for general using
PSO-for-TSP-of-51-city
- 为本人毕业设计里,粒子群算法的一个应用,求解51个城市的TSP问题,城市的个数和位置都可以改动,并与遗传算法求解的结果进行对比!-I graduated from the design, the application of the particle swarm algorithm for solving the 51 cities TSP problem, the number and location of the city can be altered, and compared with
PSO
- 针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足,本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化,并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率,提高解的精确,同时还保持了较低的计算复杂度,具有良好的稳健性。-TSP problem solving for the traditional algorithms such as genetic alg
ga-pso-aca_esn
- 用遗传粒子群蚁群混合算法来优化回升状态网络训练后的最终值。-Mixed genetic particle swarm ant colony algorithm to optimize the final value in the rebound after the state network training.
PSO-algorithm-
- 改进的 遗传算法 及其测试函数-Improved Genetic Algorithm and Its test function
PSO
- 遗传算法,用于资源优化问题,如果需要,可以下载-Genetic algorithms for resource optimization problems, if needed, can be downloaded to see