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 |
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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: |
Size: 16384 |
Author: |
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Description: 这是在最初的蚁群算法的变异算子的基础上改进的变异算子，旅行商问题中含100个城市的程序-This is the first ant colony algorithm mutation operator on the basis of improving the mutation operator, Traveling Salesman Problem with 100 cities in the process Platform: |
Size: 2048 |
Author:秦云芬 |
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Description: ：Matlab遗传算法(GA)优4~-r-具箱是基于基本操作及终止条件、二进制和十进制相互转换等操作的综合
函数库。其实现步骤包括：通过输入及输出函数求出遗传算法主函数、初始种群的生成函数，采用选择、交叉、变异
操作求得基本遗传操作函数。以函数仿真为例，对该函数优化和GA改进，只需改写函数m文件形式即可。-: Matlab genetic algorithm (GA) U 4 ~-r-a box is based on the basic operation and termination conditions, binary and decimal conversion operation, such as a comprehensive function library. The realization of these steps include: input and output functions through the genetic algorithm to derive the main function, initial population generation function, the use of selection, crossover, mutation operation to achieve the basic function of genetic manipulation. Simulation in order to function as an example, the GA function optimization and improvement, just rewritten m the form of a document can function. Platform: |
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Author:icyrock |
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Description: GA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。
本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值啦）
并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；-GA (Simple Genetic Algorithm) is a powerful, intelligent multi-variable optimization algorithms, which mimic the breeding populations of the law to be optimized. SGA can optimize this variable, and the minimum value, maximum value (when the function of the countdown you will seek the minimum value) and to support the floating-point encoding, grey code, binary code round of gambling options, tournament selection single-point crossover, uniform Cross, floating-point crossover single point mutation, floating-point variation Platform: |
Size: 9216 |
Author:yuandi |
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Description: 一种基于matlab的遗传规划编码程序 其中包含交叉和变异的代码-Based on the genetic programming matlab coding procedures of crossover and mutation which contains the code Platform: |
Size: 28672 |
Author:不吃萝卜的萝卜 |
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Description: 一种快速简单的遗传算法程序，基于Matlab7,加入特殊的交叉算子和变异算子，使算法更快。-SpeedyGA is a vectorized implementation of a genetic algorithm in the Matlab programming language.SpeedyGA has been created and tested under Matlab 7 (R14). Added mutation and crossover mask pregeneration. Platform: |
Size: 3072 |
Author:竹子的信仰 |
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Description: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作-fga.m the main program for the genetic algorithm using binary Gray encoding, roulette wheel based on the law of non-linear ranking selection, uniform crossover and mutation operations, but also the introduction of the inversion operation Platform: |
Size: 6144 |
Author:赵彦 |
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Description: 遗传算法程序说明: fga.m 为遗传算法的主程序 采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-Description of the procedures for genetic algorithms: fga.m main program for the genetic algorithm using binary Gray encoding, roulette wheel based on the law of non-linear ranking selection, uniform crossover and mutation operations, but also the introduction of the inversion operation! Platform: |
Size: 3072 |
Author:hexing |
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Description: 多目标货物配装问题是一个复杂的组合优化问题，属于NP难问题，本文用混合粒子群算法求解多目标货物配装问题。混合粒子群算法在基本粒子群算法的基础上，通过引进遗传算法中的交叉和变异的策略，避免了陷入局部最优，加快了达到全局最优的收敛速度。此外，本文提出用权重系数来平衡各目标使各目标都能达到相对较优的效果。-Multi-objective loading of goods is a complicated combinatorial optimization problems are NP hard problems, this paper, hybrid particle swarm algorithm to solve multi-objective problem loading cargo. Hybrid Particle Swarm Algorithm in elementary particle swarm optimization based on genetic algorithm through the introduction of crossover and mutation of the strategy to avoid a fall into local optimum, global optimum to achieve accelerated convergence. In addition, this paper, the weight factor used to balance the various objectives so that the objectives can be achieved relatively better results. Platform: |
Size: 6144 |
Author:廖志 |
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Description:
3D Space Coordinate Transformations
This folder contains 3 files (m-functions) :
- t2x.m Transformation Matrix to Generalized Position Vector.
- x2t.m Generalized Position Vector to Transformation Matrix.
- m2m.m Mass/Inertia Tensor transformation with coordinate change.
In the Generalized Position Vector the orientation can be expressed with:
- unit quaternion,
- Euler angles xyz (roll, pitch, and yaw),
- Euler angles zyz (rotation, precession, and mutation),
- unit vector and rotation angle,
- Denavitt-Hartemberg parameters.
Conversion between the above orientation systems can be easily achieved.
The three files work independently on each other, but since they work
on the same objects it is somewhat useful to keep them in the same folder. -
3D Space Coordinate Transformations
This folder contains 3 files (m-functions) :
- t2x.m Transformation Matrix to Generalized Position Vector.
- x2t.m Generalized Position Vector to Transformation Matrix.
- m2m.m Mass/Inertia Tensor transformation with coordinate change.
In the Generalized Position Vector the orientation can be expressed with:
- unit quaternion,
- Euler angles xyz (roll, pitch, and yaw),
- Euler angles zyz (rotation, precession, and mutation),
- unit vector and rotation angle,
- Denavitt-Hartemberg parameters.
Conversion between the above orientation systems can be easily achieved.
The three files work independently on each other, but since they work
on the same objects it is somewhat useful to keep them in the same folder.
Platform: |
Size: 6144 |
Author:kiyoung |
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Description: 基于生物免疫系统的自适应学习、免疫记忆、抗体多样性及动态平衡维持等功能,提出一种动态多目标免疫
优化算法处理动态多目标优化问题.算法设计中,依据自适应ζ邻域及抗体所处位置设计抗体的亲和力,基于Pa-
reto控制的概念,利用分层选择确定参与进化的抗体,经由克隆扩张及自适应高斯变异,提高群体的平均亲和力,利
用免疫记忆、动态维持和Average linkage聚类方法,设计环境识别规则和记忆池,借助3种不同类型的动态多目标
测试问题,通过与出众的动态环境优化算法比较,数值实验表明所提出算法解决复杂动态多目标优化问题具有较大
潜力.-:A dynamic multi-objective immune optimization algorithm suitable for dynamic multi-objective
optimization problems is proposed based on the functions of adaptive learning, immune memory, antibody
diversity and dynamic balance maintenance, etc. In the design of the algorithm, the scheme of antibody af-
finity was designed based on the locations of adaptive-neighborhood and antibody antibodies participating
in evolution were selected by Pareto dominance. In order to enhance the average affinity of the population,
clonal proliferation and adaptive Gaussian mutation were adopted to evolve excellent antibodies. Further-
more, the average linkage method and several functions of immune memory and dynamic balance mainte-
nance were used to design environmental recognition rules and the memory pool. The proposed algorithm
was compared against several popular multi-objective algorithms by means of three different kinds of dy-
namic multi-objective benchmark problems. Simulations show Platform: |
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Author:王飞 |
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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 |
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Description: 包括遗传算法的各种运算（交叉，变异）的实例源程序代码-Various operations, including genetic algorithms (crossover and mutation) of the instance of the source code Platform: |
Size: 104448 |
Author:高利敏 |
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Description: 光伏电池阵列输出功率受光照强度和温度变化的影响，因此最大功率点跟踪（MPPT）技术广泛应用于光伏系统中。在所有最大功率点(MPP)控制策略中，扰动观察（P&O）MPPT算法因易实现被广泛应用，然而它的缺点是在稳定工作状态下工作点通过MPP 时会导致能量振荡损耗，并且在光照强度或温度发生突变时表现较差的动态响应。在本文中，提出一种改进型变步长扰动观察MPPT 算法，此方法依据工作点动态调整步长变化，与传统固定步长方法比较，本文提出的方法能有效地提高MPPT 速度和转换效率，通过仿真和实验结果分析，验证了此改进算法的可行性。-PV array output power by the light intensity and temperature changes, the maximum power point tracking (MPPT) technology is widely used in photovoltaic systems. In all the maximum power point (MPP) control strategy, the disturbances observed (P & O) MPPT algorithm due to easy implementation is widely used, but its drawback is that the steady-state operating point by MPP will lead to energy oscillations loss, and in the light intensity or temperature when the poor performance of a mutation in the dynamic response. In this paper, propose a modified variable step disturbance observed MPPT algorithm, this method dynamically adjusted according to operating point step change, with the more traditional fixed-step method, the proposed method can effectively improve the MPPT speed and efficiency, The simulation and experimental results verify the feasibility of this improved algorithm. Platform: |
Size: 516096 |
Author:YUJIAN.XU |
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Description: 免疫算法求解配送中心选址问题，配送中心向需求点配送货物是供应链中的重要部分.本文以成本最低为目标函数,把距离上限加入到惩罚机制,并根据抗体和抗原之间的亲和力设计自适应交叉和变异概率,把自适应的免疫算法应用到配送中心模型中进行求解,最后通过仿真实验对比验证了算法用在配送中心选址上有较好的效果.(Immune Algorithm is used to solve the location problem of Distribution Center, which is an important part of supply chain. This paper takes the lowest cost as the objective function, adds the upper distance limit to the penalty mechanism, and designs the adaptive crossover and mutation probability according to the affinity between antibody and Antigen, the adaptive immune algorithm is applied to the distribution center model to solve the problem. Finally, the simulation results show that the algorithm is effective in the location of Distribution Center.) Platform: |
Size: 31744 |
Author:代码大小姐 |
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Search - mutation in matlab