搜索资源列表
gafuzzymatlab
- 模拟退火，禁忌搜索，遗传算法，神经网络-MATLAB程序合集 -Simulated annealing, tabu search, genetic algorithm, neural network-MATLAB collection procedures simulated annealing, tabu search, genetic algorithm, neural network-MATLAB collection procedures
wx1111
- 基于遗传和模拟退火算法的自动组卷系统设计与实现基于遗传和模拟退火算法的自动组卷系统设计与实现
SAGA
- 用模拟退火优化遗传算法，使遗传算法具有反向搜索能力，通过仿真表明能够得到更优的值。-Optimization by simulated annealing genetic algorithm, genetic algorithm so that the reverse search capabilities, through the simulation shows that can be better value.
GASA
- 图像分割的优化算法，结合了遗传算法和模拟退火算法，并将优化算法和原始otsu方法进行了比较-Optimization of image segmentation algorithm that combines genetic algorithm and simulated annealing algorithm and optimization algorithm and the original method otsu are compared
matlabSA
- matlab 模拟退火算法 内含遗传算法及迭代过程-simulated annealing algorithm matlab implicit and iterative process of genetic algorithm
123
- 绝对经典的模拟退火算法源文件 含遗传算法-Absolute classic simulated annealing algorithm with genetic algorithm source files
GODLIKE
- 遗传算法与模拟退火,粒子群算法的结合与比较实验.稍微修改就可以学习应用-Genetic algorithms and simulated annealing, particle swarm optimization algorithm and comparison with experiment. A slight modification can learn the application of
psoandimprovedpso
- 基本粒子群优化算法和改进粒子群优化算法程序，包括：用基本粒子群算法求解无约束优化问题，用带压缩因子的粒子群算法求解无约束优化问题，用线性递减权重粒子群优化算法求解无约束优化问题，用自适应权重粒子群优化算法求解无约束优化问题，用随机权重粒子群优化算法求解无约束优化问题，用学习因子同步变化的粒子群优化算法求解无约束优化问题，用学习因子异步变化的粒子群优化算法求解无约束优化问题，用二阶粒子群优化算法求解无约束优化问题，用二阶振荡粒子群优化算法求解无约束优化问题，用混沌粒子群优化算法求解无约束优化问题，
GASA
- 基于遗传模拟退火和Otsu法的图像分割。采用一种基于模拟退火算法(SA)的混合遗传算法(GASA),来进行Otsu方法选择阈值，其中进行求解时是非线性的遗传算法（GA）的应用优化了求阈值过程，并尽可能地减少其运算量,而加入SA则避免了遗传算法(GA)常见的早熟收敛现象,从而实现有效简单的图像分割。 -adopt hybrid genetic algorithm(GASA) based on simulated annealing algorithm,choose threshold by O
The_Intelligent_Optimization_Methods
- 有关智能优化方法(模拟退火,遗传算法,禁忌搜索,等等)-The intelligent optimization methods (simulated annealing,genetic algorithm,tabu search,etc)
TSP
- 有遗传算法及模拟退火算法对TSP的应用，有框架窗口。-A genetic algorithm and simulated annealing algorithm for TSP applications, there are frame window.
PIDcontrowithAOC
- 除了蚁群算法，可用于PID参数优化的智能算法还有很多，比如遗传算法、模拟退火算法、粒子群算法、人工鱼群算法，等等。-In addition to ant colony algorithm can be used to optimize the PID parameters there are many intelligent algorithms, such as genetic algorithms, simulated annealing algorithm, particle swarm
Solving
- 求解双层规划问题常用的算法有极点算法、直接搜索法、下降法和非数值优化方法(如模拟退火算法、遗传算法等)，遗传算法的求解思路是：首先对上层的决策变量编码，代人下层规划模型，通过求解下层模型的决策变量值，代入上层模型计算适应度值，然后进行交叉、变异、选择操作，最后求出最优解。-Solving Bilevel Programming Problems with pole algorithm commonly used algorithms, direct search method, descent
geneticalgorithmandsimulatedannealingalgorithm
- 建立了多约束条件车辆路径问题的数学模型和求解流程先采用最近插入法生成初始解然后基于遗传算法和模拟退火算法改进初始解-The establishment of a multi-vehicle routing problem with constraint model and solution process first insertion generated by the recent initial solution and based on genetic algorithm and simu
taiozhanbei
- 用遗传算法和模拟退火算法解决旅行商方面的问题-The genetic algorithm and simulated annealing algorithm to solve traveling salesman problems
SimulatedAnnealing
- 本文介绍了模拟退火算法的原理和求解方法,并将其用于指数曲线的拟合,在Matlab语言环境下实现了该算法,并且与文献中的遗传算法、线性回归相比较。数值模拟结果表明,该算法能更好地实现最优拟合。-This article describes the principle of simulated annealing algorithm and solution methods, and used an exponential curve fitting, the language environmen
TSP
- 用模拟退火算法和遗传算法实现TSP旅行商问题，并可以用Matlab对结果进行图形显示分析，非常实用于初学者-Using simulated annealing algorithm and genetic algorithm traveling salesman problem TSP, and the results can be used Matlab graphics analysis, very useful for beginners
MCMProjects
- 我备战2010数学建模美赛所精心准备的算法资料，一共13个算法。应该说是目前比较全的算法集了。每个算法由一个VC6例子实现，来解决一个问题。其中一些是自己编写，其它的也是由网上找到后经过修改编译通过的。比赛结果还不错，一个M，现在把资料共享出来，希望对大家有所帮助，算法主要有模拟退火，遗传算法，蒙特卡罗算法，蚁群算法，粒子群算法，元胞自动机，Dijkstra，最小生成树算法，二分图最大匹配算法，最大流算法，动态规划算法，分支定界法，排队论算法，决策论算法等，我写了个说明文件，列的挺详细的-I a
GA_test
- 遗传算法：二进制、单点交叉、单点变异、轮盘赌、模拟退火(默认无)、最优个体保留(默认无)-Genetic algorithm: binary, single-point crossover, single-point mutation, roulette, simulated annealing (default no), best individual reservations (default no)
QosMulticastMATLAB
- 功能代码含多种QoS约束的组播路由问题遗传模拟退火算法通用MATLAB源码.-multicast