Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).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。详细的步骤以后的章节介绍 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-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross - (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
Size: 16633 |
Author:张正 |
Hits:
Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).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。详细的步骤以后的章节介绍 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-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications Platform: |
Size: 16384 |
Author: |
Hits:
Description: This an implementation of Particle Swarm Optimization algorithm using
the same syntax as the Genetic Algorithm Toolbox, with some additional
options specific to PSO. Allows code-reusability when trying different
population-based optimization algorithms. Certain GA-specific parameters
such as cross-over and mutation functions will not be applicable to the
PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox.-This is an implementation of Particle Swarm Optimization algorithm using
the same syntax as the Genetic Algorithm Toolbox, with some additional
options specific to PSO. Allows code-reusability when trying different
population-based optimization algorithms. Certain GA-specific parameters
such as cross-over and mutation functions will not be applicable to the
PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox. Platform: |
Size: 4096 |
Author:Chris Leung |
Hits:
Description: 1．本程序演示的是用简单遗传算法随机一个种群，然后根据所给的交叉率，变异率，世代数计算最大适应度所在的代数
2．演示程序以用户和计算机的对话方式执行，即在计算机终端上显示“提示信息”之后，由用户在键盘上输入演示程序中规定的命令；相应的输入数据和运算结果显示在其后。
3．测试数据
输入初始变量后用y=100*(x1*x1-x2)*(x1*x2-x2)+(1-x1)*(1-x1)其中-2.048<=x1,x2<=2.048作适应度函数求最大适应度即为函数的最大值
-1. This program demonstrates a simple genetic algorithm is a random population, then according to the crossover rate and mutation rate, fitness for calculating the maximum number of generations in which algebra 2. Demo program to computer users and the implementation of dialogue, that is displayed on a computer terminal " message" after the keyboard input by the user specified in the order demo program the corresponding input data and results in a subsequent operation. 3. Test data input with the initial variables y = 100* (x1* x1-x2)* (x1* x2-x2)+ (1-x1)* (1-x1) where-2.048 < = x1, x2 < = 2.048 ask for the maximum fitness function is the function of the maximum fitness Platform: |
Size: 10562560 |
Author:季琳 |
Hits:
Description: 自然计算中遗传算法的各个程序，matlab环境下开发的源代码。best.m 求种群中适应度最大的值
calfitvalue.m 计算每个个体的适应度
calobjvalue.m 适应度函数
crossover.m 交叉变换
decodebinary.m 将二进制数转换成十进制数
decodechrom.m 将二进制数转换成十进制数
initpop.m 产生初始种群
mutation.m 变异
selection.m 选择合适的个体进行复制
main.m 主函数
-Nature of each genetic algorithm calculation procedures, matlab environment with source code. best.m find the largest population in the fitness value of calfitvalue.m calculated for each individual' s fitness calobjvalue.m fitness function crossover.m cross-conversion decodebinary.m Converts a binary number into decimal number decodechrom.m Converts a binary number into decimal number initpop.m generate initial population mutation.m variation selection.m select the appropriate individual to copy main.m primary function Platform: |
Size: 3072 |
Author:王芳 |
Hits:
Search - function of mutation with matlab