Description: 粒子群优化算法（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R．
Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。源于对鸟群捕食的行为研究。粒子群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解．
-Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution. Platform: |
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Author:周荷 |
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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: |
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Author:张正 |
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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: |
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Author: |
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Description: 粒子群优化算法（PSO）是一种进化计算技术（evolutionary computation）,由Eberhart博士和kennedy博士于1995年提出 (Kennedy J，Eberhart R．
Particle swarm optimization．Proceedings of the IEEE International Conference on Neural Networks．1995．1942~1948.)。源于对鸟群捕食的行为研究。粒子群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解．
-Particle Swarm Optimization (PSO) is an evolutionary computation techniques (evolutionary co mputation) by Dr. Eberhart and kennedy Dr. raised in 1995 (Kennedy, J., Eberhart R. Particle swarm optimization.Proc eedings of the IEEE International Conference o n Neural Networks.1995.1942 ~ 1948.) . From the flock of the predatory behavior. PSO algorithm is the basic idea of individual groups through the sharing of information and collaboration to find the optimal solution. Platform: |
Size: 134144 |
Author:周荷 |
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Description: 这是一个pso程序源代码，pso源于对鸟群捕食行为的研究而发明的进化计算技术，属于进化算法的一种。
优点：收敛速度快，具有全局寻优能力，而且编程简单，易于推广使用。 -This is a pso source code, pso out of the flock of predatory behavior and evolutionary computation invention of the technology is an evolutionary algorithm. Advantages : fast convergence with global optimization capability and programming simple and easy to use. Platform: |
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Author:张清 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar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。
-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools. Platform: |
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Author:叶开 |
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Description: 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。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 computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications Platform: |
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Author:zzh |
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Description: 这是多目标进化算法包，这个集成包包含了近期的流行的，常用的多目标进化算法，包括NSGA2,SPEA2,PEAS2,以及多目标粒子群算法，另外还集成了单目标进化算法。-This is a multi-objective evolutionary algorithm package, the integrated package includes a recent popular, commonly used in multi-objective evolutionary algorithm, including NSGA2, SPEA2, PEAS2, as well as multi-objective particle swarm optimization, in addition to an integrated single-objective evolutionary algorithm. Platform: |
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Author:宋武 |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。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] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications. Platform: |
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Author:wzy |
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Description: 微粒群优化算法 （PSO） 是一种进化计算技术 , 由Eberhart博士和kennedy博士于1995年提出。微粒群优化算法的基本思想是通过群体中个体之间的协作和信息共享来寻找最优解。
-Particle Swarm Optimization (PSO) is an evolutionary computation technique, by Dr. Eberhart and Dr. kennedy made in 1995. Particle Swarm Optimization Algorithm The basic idea of the individual through group collaboration and information sharing between to find the optimal solution. Platform: |
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Author:Kevin |
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Description: 粒子群优化算法是一种进化优化技术，源于对鸟群扑食的行为，是一种基于迭代的优化工具。此文件提供了基本粒子群算法、带压缩因子的粒子群算法、二阶粒子群算法、二阶振荡粒子群算法、权重改进的粒子群算法、混沌粒子群算法、基于杂交的粒子群算法、基于模拟退火的粒子群算法的MATLAB源代码。-PSO is an evolutionary optimization technique, derived from the behavior of the birds of prey, is based on iterative optimization tools. This document provides basic particle swarm algorithm, with a compression factor of the particle swarm algorithm, particle swarm optimization order, second order oscillating particle swarm algorithm, the weight particle swarm algorithm, chaotic particle swarm algorithm, based on hybrid particle swarm algorithm, based on Simulated annealing particle swarm optimization MATLAB source code. Platform: |
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Author:熊杰 |
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Description: The economic load dispatch plays an important role in the operation of power system,
and several models by using different techniques have been used to solve these problems.
Several traditional approaches, like lambda-iteration and gradient method are utilized to find out
the optimal solution of non-linear problem. More recently, the soft computing techniques have
received more attention and were used in a number of successful and practical applications. The
purpose of this work is to find out the advantages of application of the evolutionary computing
technique and Particle Swarm Optimization (PSO) in particular to the economic load dispatch
problem. Here, an attempt has been made to find out the minimum cost by using PSO using the
data of three and six generating units Platform: |
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Author:amijeet |
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Description: 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，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 computing (evolutionary computation), there is invented by Dr. Eberhart and Dr. kennedy. From the behavior of birds of prey. PSO with genetic algorithm is similar to an iteration-based optimization tool. System is initialized to a group of random solutions, the optimal value by iterative search. But there is no genetic algorithm with the cross (crossover) and mutation (mutation). But the particles in the solution space to follow the optimal particle search. Comparison with genetic algorithms, PSO has the advantage of simple and easy to implement and there is no need to adjust many parameters. Has been widely used in function optimization, neural network training, fuzzy system control, and other genetic algorithm applications. Platform: |
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Author:天涯 |
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Description: PSO 算法属于进化算法的一种，和遗传算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优-PSO algorithm is an evolutionary algorithm, and genetic algorithm is similar, it is starting from a random solution, by iteration to find the optimal solution, it is also to evaluate the fitness of the solution by the quality, but it is much simpler than the rules of the genetic algorithm, It is not genetic algorithms " cross" (Crossover) and " variant" (Mutation) operations, which by following the optimal value of the current search to find the global optimum Platform: |
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Author:shitou |
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Description: 粒子群算法是一种进化计算技术，来源于对鸟群捕食的思考,最早由Kenney与Eberhart
于1995年提出。在PSO中，寻找最优解被看做群体寻找目标。个体在搜索的过程中具有自己的位置和搜索速度。个体追寻最优个体在解空间中进行搜索。搜索的过程是一个反复的迭代过程。在这个过程中，个体完成的任务一是找寻自己认可的最优解；另个任务是获知群体得到的暂时最优解。
-The particle swarm optimization is an evolutionary computation technique, derived from the thinking of the birds of prey, was first proposed by Kenney and Eberhart in 1995. PSO, to find the optimal solution is seen as a group to find the target.The individual has its own position in the search process and search speed. The individual pursuit of the best individual in the solution space to search. The search process is an iterative process repeated. In this process, individuals complete the task first, find the optimal solution to themselves another task is informed groups of temporary optimal solution. Platform: |
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Author:刘鹏飞 |
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Description: 粒子群优化(Particle Swarm Optimization, PSO)，又称微粒群算法，是由J. Kennedy和R. C. Eberhart等于1995年开发的一种演化计算技术,来源于对一个简化社会模型的模拟。-Particle Swarm Optimization (Particle Swarm Optimization, PSO), also known as particle swarm optimization, by J. Kennedy and RC Eberhart equal to 1995 developed an evolutionary computing technology, derived from the simulation of a simplified social model. Platform: |
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Author:Tony Cheung |
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Description: 为进一步提高螺栓遗传算法的优化效率，加速寻优过程，提出基于对立策略的螺栓遗传算法。该算法在种群初始化阶段和变异阶段均用对立取代随机方式，提高产生解的质量。利用测试函数对算法的效率进行检验，将其与差分算法、遗传算法、粒子群算法和螺栓遗传算法进行对比，结果表明，新算法具有更快的收敛速度和更高的求解精度。-In order to improve the performance of Stud Genetic Algorithm(SGA) and accelerate the convergence speed, an improved stud genetic
algorithm based on opposition is proposed. Conventional random method is replaced with opposition method in both population initialization and
mutation, which can improve the quality of solutions. Based on benchmark functions, the optimization performance of the algorithm is compared
with genetic algorithm, different evolutionary, particle swarm optimization and stud genetic algorithm, the results show that the new algorithm has better optimization performance. Platform: |
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Author:zhangyan |
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Description: 一种改进的变参数粒子群优化算法，该方法以进化状态因子计算策略和进化状态估计模型-An improved particle swarm optimization variable parameters, the method to calculate evolutionary strategies and evolutionary state factor model of state estimation Platform: |
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Author:燕子 |
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Search - an evolutionary particle