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Description: 粒子群优化算法！！！
系统地介绍了粒子群优化算法,归纳了其发展过程中的各种改进如惯性权重!收敛因子!跟踪并
优化动态目标等模型\"阐述了算法在目标函数优化!神经网络训练!模糊控制系统等基本领域的应用并
给出其在工程领域的应用进展,最后,对粒子群优化算法的研究和应用进行了总结和展望,指出其在计算
机辅助工艺规划领域的应用前景\"-PSO algorithm! ! ! A systematic introduction to PSO algorithm, summed up its development process such as the improvement of inertia weight! Convergence factor! track and dynamic optimization model objectives, "explained the algorithm optimization objective function! Neural Network Training! Fuzzy Control System basic areas of application and gives the project from its the application domain, finally, the PSO algorithm research and application of the summary and outlook. pointed out in the field of computer-aided process planning applications prospects "
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Size: 53571 |
Author: 八云 |
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Description: 粒子群优化算法！！！
系统地介绍了粒子群优化算法,归纳了其发展过程中的各种改进如惯性权重!收敛因子!跟踪并
优化动态目标等模型"阐述了算法在目标函数优化!神经网络训练!模糊控制系统等基本领域的应用并
给出其在工程领域的应用进展,最后,对粒子群优化算法的研究和应用进行了总结和展望,指出其在计算
机辅助工艺规划领域的应用前景"-PSO algorithm! ! ! A systematic introduction to PSO algorithm, summed up its development process such as the improvement of inertia weight! Convergence factor! track and dynamic optimization model objectives, "explained the algorithm optimization objective function! Neural Network Training! Fuzzy Control System basic areas of application and gives the project from its the application domain, finally, the PSO algorithm research and application of the summary and outlook. pointed out in the field of computer-aided process planning applications prospects "
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Size: 53248 |
Author: 八云 |
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Description: 围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.-particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.
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Size: 76800 |
Author: wanglg |
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Description: Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.-Swarm intelligence algorithms are based on naturalbehaviors. Particle swarm optimization (PSO) is astochastic search and optimization tool. Changes in thePSO parameters, namely the inertia weight and thecognitive and social acceleration constants, affect theperformance of the search process. This paper presents anovel method to dynamically change the values of theseparameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing thevalues of the PSO parameters.
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Size: 365568 |
Author: sky |
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Description: 带有收缩因子和惯性权重的基本PSO粒子群算法源代码。本源代码模块化编写，结构清晰，便于改进和做数值实验-With contraction factor and inertia weight PSO basic particle swarm algorithm source code. Source code modular preparation, structure, clear, easy to improve and to do numerical experiments
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Size: 3072 |
Author: 楚湘华 |
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Description: 改进PSO-SVM在说话人识别中的应用。通过对粒子群优化算法中惯性权重和全局最优值
的分析，提出了一种根据迭代次数而自适应变化的惯性权重的粒子群优化方法-Improvement in the PSO-SVM speaker recognition applications. Through particle swarm optimization algorithm in the inertia weight and the analysis of the global optimum value, a number of iterations in accordance with changes in the adaptive inertia weight particle swarm optimization method
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Size: 315392 |
Author: 彭伟 |
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Description: 程序说明：
jblzq.m为基本粒子群程序求函数的最大值
lzq2.m是惯性权重法求函数的最大值
lzq3.m是惯性权重法求函数最小值。当然也可以用lzq2.m实现，在函数前加个负号
lzq4.m是收敛因子法求函数最小值
lzq5.m是带变异的惯性权重法求最大值，变异条件比较简单，变异次数多。还有一种变异是利用各粒的最优位置与全局最优位置的差的平方和，再开根号的值小于某一值最为收敛条件，将在我的报告中叙述
lzq6.m是分层粒子群优化算法，即利用两个粒子群同时进行搜索，一个变异快的种群善于全局搜索，另一个种群善于局部搜索。
lzq7.m是将lzq6.m进行简化，即用三维数组存储两个粒子群的参数。-Procedure Description:
jblzq.m demand function for the elementary particle group of programmers and the maximum of
lzq2.m is a function of inertia weight method seeking the maximum of
lzq3.m is a function of inertia weight method seeking the minimum value
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Size: 7168 |
Author: 沈龙 |
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Description: 最新SD期刊上关于改进型PSO算法，我是通过学校内部数据库在SD下载的哦！包括《A dynamic inertia weight particle swarm optimization algorithm》、《Adaptive Particle Swarm Optimization》、《Cyber Swarm Algorithms – Improving particle swarm optimization using adaptive memory strategies》。这三篇都是比较有研究价值的学术文章，识货的请下载！-Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algo-rithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 diff erent
dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search perfor-mance on the benchmark functions signifi cantly.
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Size: 2744320 |
Author: asdwe |
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Description: An Improved PSO Algorithm to Optimize BP Neural Network
Abstract
This paper presents a new BP neural network
algorithm which is based on an improved particle swarm
optimization (PSO) algorithm. The improved PSO (which
is called IPSO) algorithm adopts adaptive inertia weight
and acceleration coefficients to significantly improve the
performance of the original PSO algorithm in global
search and fine-tuning of the solutions. This study uses the
IPSO algorithm to optimize authority value and threshold
value of BP nerve network and IPSO-BP neural network
algorithm model has been established. The results
demonstrate that this model has significant advantages
inspect of fast convergence speed, good generalization
ability and not easy to yield minimal local results
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Size: 252928 |
Author: dasu |
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Description: 带有收缩因子和惯性权重的基本PSO粒子群算法源代码。本源代码模块化编写，结构清晰，便于改进和做数值实验
-With the shrinkage factor and the basic PSO inertia weight particle swarm algorithm source code. Modular source code written in a clear structure, easy to improve and to do numerical experiments
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Size: 14336 |
Author: 王兰彬 |
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Description: 函数调用求解三维空间中的最优解 采用惯性权重递变的粒子群算法-use PSO serve the normal problem by transmutation of inertia weight
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Size: 1024 |
Author: 杨于漫 |
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Description: 惯性PSO算法是个常用的PSO算法,具有高效性,十分简便易懂-Inertia PSO algorithm is commonly used in the PSO algorithm is highly efficient, very easy to understand
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Size: 2048 |
Author: houlvlin |
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Description: 当惯性权值不变的情况下取不同的值，c取值为1.5和2-When the inertia right under the condition of invariable value take different values
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Size: 77824 |
Author: 周文江 |
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Description: 经典粒子群算法，经过多次优化，演示出图， 待优化的目标函数：N
粒子数目：N
惯性权重：w
学习因子：c1,c2
最大迭代次数：M
问题的维数：D
目标函数取最小值时自变量值：xm
目标函数的最小值：fv-Classical particle swarm algorithm, optimized for many times, demonstrates plotting objective function to be optimized: N of the number of particles: N inertia weight weight: w learning factor: c1, c2 maximum number of iterations: M dimension independent variables: D objective function is to take the minimum value: xm minimum value of the objective function: fv
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Size: 1024 |
Author: 杜青青 |
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Description: 粒子群优化算法及其参数设置如惯性权值，加速因子的设置对算法基本性能的影响-Particle swarm optimization algorithm and its parameters, such as inertia weight, acceleration factor setting the basic performance of the algorithm
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Size: 1024 |
Author: 周文 |
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Description: pso在惯性权重，学习因子，位置更新公式的改进-
PSO inertia weight, location update formula and improve procedures on the learning factor
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Size: 2048 |
Author: 李俊杰 |
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Description: PSO algorithm with time varying inertia weight
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Size: 1024 |
Author: umesh |
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Description: 自己写的IPSO单目标搜索算法 内部已经带有4个测试函数以供测试 使用了拉丁方设计方法 和 自适应惯性权重-Write your own internal IPSO single target search algorithm has four test functions for testing with the use of the Latin square design and adaptive inertia weight
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Size: 2048 |
Author: MuW |
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Description: 针对普通粒子群算法改进，通过改进惯性权值的下降速度，加快收敛速度。-For ordinary particle swarm optimization improved by improving the rate of decline inertia weight, speed up the convergence.
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Size: 5120 |
Author: 袁杰 |
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Description: 带惯性权重的pso matlab代码程序(PSO matlab code program with inertia weight)
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Size: 1024 |
Author: jerry00000
|
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