Description: 简洁得模拟退火算法，用来求函数得极值问题，有兴趣得可以看看。里面提出了一个问题，有兴趣得可以做一个实验-concise in simulated annealing algorithm, used to function in extreme demand, are interested they can look at. They raised a question that interested you can do an experiment Platform: |
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Author:张庆 |
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Description: 由于K－均值聚类算法局部最优的特点，而模拟退火算法理论上具有全局最优的特点。因此，用模拟退火算法对聚类进行了改进。20组聚类仿真表明，平均每次对K结果值改进8次左右，效果显著。下一步工作：实际上在高温区随机生成邻域是个组合爆炸问题（见本人上载软件‘k－均值聚类算法’所述），高温跳出局部解的概率几乎为0，因此正考虑采用凸包约束进行模拟聚类，相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the 'k-means clustering algorithm' mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies. Platform: |
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Author:韩磊 |
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Description: 模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S—R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S 'S' = S+ sign (). delta where given for the step, [-1,1] Random Number Platform: |
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Author: |
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Description: 以一个简单的例子说明模拟退火算法的思想。 模拟退火法求函数f(x,y) = 5sin(xy) + x^2 + y^2的最小值，对理解模拟退火算法是一个很好的程序示例。-to a simple example illustrates the simulated annealing algorithm thinking. Simulated Annealing for the function f (x, y) = 5sin (xy) x ^ 2 y ^ 2 minimum, the right understanding of simulated annealing method is a good example of the procedure. Platform: |
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Author:小刘 |
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Description: 模拟退火算法是为了避免求解最优化出现局部极值的问题而提出的算法，保证最终的结果是全局最优的，该matlab源程序能在matlab环境中实现-simulated annealing method is the best solution in order to avoid a partial optimization of extreme concern raised by the algorithm to ensure that the final result is that the global optimum, the source Matlab can achieve Matlab environment Platform: |
Size: 5120 |
Author:yj |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 9216 |
Author:刘明 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。
-simulated annealing algorithm derived from solid annealing method, the heating to the full solid, let its slowly cooling, heating, solid particles with internal temperature rise-into disorder, which can increase, and slowly cooling gradual and orderly particles in each temperature has reached equilibrium, in the end when the temperature reached to ground state, which can be reduced to the minimum. According to the Metropolis criteria particles at a temperature T leveling the probability of e- E/(kT), in which the E-T when the temperature within, E capacity for change, for the Boltzmann constant k. Solid simulated annealing combinatorial optimization problems, will be able to target E simulation function f, T evolved temperature control parameters t, that is to be solving combinatorial o Platform: |
Size: 6144 |
Author:刘明 |
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Description: For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix.
heuristics: descent.
Tests: 100 queens in less than 1 second and 67 iterations. 500 queens in 1 second and 257 iterations. 1000 queens in 11 seconds and 492 iterations.
heuristics: Simulated annealing.
Tests: 100 queens in less than 1 second and 47 iterations. 500 queens in 5 seconds and 243 iterations. 1000 queens in 13 seconds and 497 iterations.
heuristics: based on Simulated Annealing.
Tests: 100 queens in less than 1 second and 60 iterations. 500 queens in 1 second and 224 iterations. 1000 queens in 5 seconds and 459 iterations. 10 000 queens in 20 minutes 30 seconds and 4885 iterations.
-For the incomplete methods, we kept the representation of the queens by a tab le and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems th an the representation by a matrix. heuristics : descent. Tests : 100 queens in less than a second and 67 iteration s. 500 queens in a second and 257 iterations. 100 queens 0 in 11 seconds and 492 iterations. heuri stics : Simulated annealing. Tests : 100 queens in less than a second and 47 iteration s. 500 queens in 5 seconds and 243 iterations. 10 00 queens in 13 seconds and 497 iterations. heur istics : based on Simulated Annealing. Tests : 100 queens in less than a second and 60 iteration s. 500 queens in a second and 224 iterations. 100 0 queens in 5 seconds and 459 iterations. q 1 Platform: |
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Author:ZHU |
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Description: 这是一款基于vc编写的模拟退火算法，界面简洁易用，并附有源代码，希望对大家学习有所帮助-This is a vc prepared based on simulated annealing algorithm, the interface simple and easy-to-use, with source code, and they hope to help them to learn Platform: |
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Author:yaoxingyu |
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Description: 模拟退火工具箱，里面含有很全的matlab语言模拟退火算法-Simulated Annealing toolbox, which contains a very wide language of the simulated annealing algorithm matlab Platform: |
Size: 75776 |
Author:李娜 |
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Description: TSP can be defined as a problem where starting from a node it is required to visit every other node only once in a way that the total distance covered is minimized. After an initial solution a neighborhood solution is selected by an exchange of a randomly selected pair of nodes. The randomly generated neighbor solution is selected if it improves the solution else it is selected with a probability that depends on the extent to which it deteriorates from the current solution.-TSP can be defined as a problem where starting from a node it is required to visit every other node only once in a way that the total distance covered is minimized. After an initial solution a neighborhood solution is selected by an exchange of a randomly selected pair of nodes. The randomly generated neighbor solution is selected if it improves the solution else it is selected with a probability that depends on the extent to which it deteriorates from the current solution. Platform: |
Size: 13312 |
Author:amilcar |
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Description: 一种新型的算法，将模拟退火法和遗传算法相结合，用于求解预测问题-A new algorithm, simulated annealing and genetic algorithm are combined to solve the prediction problem for Platform: |
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Author:秦朗 |
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Description: 模拟退火的matlab实现程序，含原始的输入数据，即各个城市的具体坐标。-Simulated annealing to achieve the matlab program, including the original input data, namely the specific coordinates of each city. Platform: |
Size: 2048 |
Author:莫轻 |
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Search - simulated annealing to