Search - Optimization by Simulated Annealing

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[matlab] chap5_2 DL : 0: 实数编码遗传算法求函数极大值，实现求Rosenbrock函数极大值的优化计算的实数编码遗传算法-Real-coded genetic algorithm for maximum function, Rosenbrock function realize maximum value for the optimization calculation of real-coded genetic algorithm
Date : 2026-01-21 Size : 1kb User : 纷纷
[matlab] MCMProjects DL : 0: 我备战2010数学建模美赛所精心准备的算法资料，一共13个算法。应该说是目前比较全的算法集了。每个算法由一个VC6例子实现，来解决一个问题。其中一些是自己编写，其它的也是由网上找到后经过修改编译通过的。比赛结果还不错，一个M，现在把资料共享出来，希望对大家有所帮助，算法主要有模拟退火，遗传算法，蒙特卡罗算法，蚁群算法，粒子群算法，元胞自动机，Dijkstra，最小生成树算法，二分图最大匹配算法，最大流算法，动态规划算法，分支定界法，排队论算法，决策论算法等，我写了个说明文件，列的挺详细的-I am preparing for 2010 U.S. race mathematical modeling algorithms are well-prepared data, a total of 13 algorithms. It should be said is a relatively wide set of algorithms. Each algorithm implementation from a VC6 example, to solve a problem. Some of which I have written, the other is modified by the web, compiled by the post. Results also good, a M, is now sharing the information out, we want to help, mainly simulated annealing algorithm, genetic algorithm, Monte Carlo algorithm, ant colony algorithm, particle swarm optimization, cellular automata, Dijkstra, minimum spanning tree algorithm, the maximum bipartite matching algorithm, maximum flow algorithm, dynamic programming, branch and bound method, queuing theory algorithms, decision theory algorithm, I wrote a documentation out very detailed
Date : 2026-01-21 Size : 1.29mb User : 一招鲜
[matlab] accessory_parameter DL : 0: lingjian.m-----蒙特卡罗方法 lingjian.m使用零件初始值，用蒙特卡罗方法算出总费用。其中使用了自己编制的正态分布随机数发生器产生正态分布随机数。lingjian.m是对蒙特卡罗方法的一次练习。 accyouhua为标定值的函数，而lingjian不是一个函数，在其中已给出了一组标定值的值。退火确定标定值/unitanneal()----模拟退火连续型多个变量组合优化问题这是对模拟退火方法的一次练习，结果证明模拟退火确实是一个行之有效的方法。当参数选择较好时（一般也伴随着运行时间的加长），模拟退火的结果较好，然而用MATLAB的FMIMCON()一般可达到更高的精度。-lingjian.m----- Monte Carlo method lingjian.m part the initial value, using the Monte Carlo method to calculate the total cost. The use of the preparation of their own normal distribution random number generator to produce a normal distribution of random numbers. lingjian.m yes to the first practice of the Monte Carlo method. accyouhua calibration function, and lingjian is not a function, which has given the value of a set of calibration values . Annealing to determine the calibration value/unitanneal ()---- Simulated Annealing Combinatorial optimization problem of continuous multiple variables This is a practice of simulated annealing method, the results show that simulated annealing is an effective method. Parameter selection is better (usually accompanied by longer running time) the results of simulated annealing, however the MATLAB FMIMCON () generally achieve higher accuracy.
Date : 2026-01-21 Size : 3kb User : 王彦钧
[matlab] Optimization-by-simulated-annealing DL : 0: Optimization by simulated annealing genetic algorithm, genetic algorithm so that the reverse search capabilities, through the simulation shows that can be better value.
Date : 2026-01-21 Size : 13kb User : eaclicker
[matlab] Simulated-annealing--particle DL : 0: Simulated annealing- particle swarm optimization, the program will be simulated annealing algorithm and particle swarm optimization by combining optimization parameters have a good effect
Date : 2026-01-21 Size : 1kb User : eaclicker
[matlab] PSO DL : 0: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO，是近年来发展起来的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，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。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。-Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm.
Date : 2026-01-21 Size : 2kb User : 艾岳巍
[matlab] pso-bp DL : 0: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO，是近年来发展起来的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，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。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。 BP（Back Propagation）神经网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hidden layer)和输出层(output layer)。-Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years (Evolutionary Algorithm- EA). Kind, and simulated annealing algorithm PSO algorithm is similar evolutionary algorithms, it is also starting a random solution, through an iterative search for the optimal solution, which is also used to uate the quality through fitness solution, but it is simpler than genetic algorithm rules It has no genetic algorithm " crossover" (Crossover) and " variant" (Mutation) operation, which by following the current search to find the optimal value to the global optimum. This algorithm is its easy implementation, high accuracy, fast convergence, etc. attracted academic attention and show its superiority in solving practical problems. PSO algorithm is a parallel algorithm. BP (Back Propagation) neural network is a 1986 team of scientists headed by Rumelhart and McC
Date : 2026-01-21 Size : 2kb User : 艾岳巍
[matlab] Simulated-annealing-algorithm DL : 0: 模拟退火算法，是通过赋予搜索过程一种时变且最终趋于零的概率突跳性，从而可有效避免陷入局部极小并最终趋于全局最优的串行结构的优化算法。-Simulated annealing algorithm is a time-varying and ultimately approach zero probability of sudden rebound, which can effectively avoid the local minimum by giving the search process and, ultimately, global optimization algorithm has been optimized serial structure.
Date : 2026-01-21 Size : 7kb User : May
[matlab] Particle-Swarm-Optimization DL : 0: PSO 算法属于进化算法的一种，和模拟退火算法相似，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-PSO algorithm is a kind of evolutionary algorithms，Similar and simulated annealing algorithm，It is also starting the random solution，To find the optimal solution by iteration，It is also through the fitness to uate the quality of the solution
Date : 2026-01-21 Size : 4kb User : 翔子
[matlab] POS DL : 0: 粒子群算法（PSO）属于进化算法的一种，和模拟退火算法相似，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。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。该程序适用于MATLAB中粒子群算法的实现。- Similar to the one of simulated annealing algorithm and particle swarm optimization (PSO) belongs to the evolutionary algorithm, it is also a departure random solutions, through iterative find the optimal solution, it is also uated by the fitness of the quality of the solution, but it is more than Genetic Algorithm Rules more simple, it does not have the genetic algorithm cross (crossover) and variation (mutation) operation, follow it through to the current search to find the optimal value of the global optimum. This algorithm is its easy implementation, high precision, rapid convergence, etc. attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm. This procedure applies to implement MATLAB Particle Swarm Optimization.
Date : 2026-01-21 Size : 1kb User : 原文宾
[matlab] liziqunsuanfa DL : 0: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO，是近年来由J. Kennedy和R. C. Eberhart等[1] 开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，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。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。-Swarm optimization, also known as PSO (Particle Swarm Optimization), abbreviated as PSO, in recent years, one J. Kennedy and RC Eberhart et al. [1] developed a new evolutionary algorithm (Evolutionary Algorithm- EA). One of PSO algorithm and simulated annealing algorithm is similar to evolutionary algorithms, it is also a departure random solutions, through iterative find the optimal solution, it is also uated by the fitness of the solution quality, but it' s simpler than genetic algorithm rules it is no genetic algorithm " cross" (crossover) and " variation" (mutation) operation, follow it through to the current search to find the optimal value of the global optimum. This algorithm is its easy implementation, high precision, rapid convergence, etc. attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm.
Date : 2026-01-21 Size : 1kb User : snowtiger
[matlab] graph-coloring DL : 0: matlab编写的图着色算法，并用智能优化算法（模拟退火）去寻找其图着色的最优解-Matlab prepared by the graph coloring algorithm, and using intelligent optimization algorithm (simulated annealing) to find the optimal solution of the coloring of the graph
Date : 2026-01-21 Size : 6kb User : HMCFD
[matlab] 粒子群 DL : 0: 粒子群算法，也称粒子群优化算法或鸟群觅食算法（Particle Swarm Optimization），缩写为 PSO，是近年来由J. Kennedy和R. C. Eberhart等开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，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。(Particle Swarm Optimization, also known as Particle Swarm Optimization or Particle Swarm Optimization, abbreviated as PSO, is a new evolutionary algorithm developed by J. Kennedy and RC Eberhart in recent years (Evolutionary Algorithm - EA). ). The PSO algorithm is a kind of evolutionary algorithm. It is similar to the simulated annealing algorithm. It also starts from the random solution and finds the optimal solution through iteration. It also evaluates the quality of the solution by fitness, but it is simpler than the rules of genetic algorithm. It does not have the "crossover" and "mutation" operations of the genetic algorithm, which seeks global optimality by following the current searched optimal values. This kind of algorithm has attracted the attention of academic circles because of its advantages of easy implementation, high precision and fast convergence)
Date : 2026-01-21 Size : 5kb User : 李彤tongtong