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:qianyg |
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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: 5599 |
Author:yj |
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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: |
Size: 55296 |
Author: |
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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: 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 Platform: |
Size: 1024 |
Author:纷纷 |
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Description: 用模拟退火优化遗传算法，使遗传算法具有反向搜索能力，通过仿真表明能够得到更优的值。-Optimization by simulated annealing genetic algorithm, genetic algorithm so that the reverse search capabilities, through the simulation shows that can be better value. Platform: |
Size: 12288 |
Author:史峰 |
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Description: 模拟退火-粒子群算法，该程序将模拟退火算法和粒子群算法相结合，对优化参数有很好的效果-Simulated annealing- particle swarm optimization, the program will be simulated annealing algorithm and particle swarm optimization by combining optimization parameters have a good effect Platform: |
Size: 1024 |
Author:liwei |
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Description: 一个很好用的自适应遗传算法，用于一维参数优化，计算速度快-Simulated annealing- particle swarm optimization, the program will be simulated annealing algorithm and particle swarm optimization by combining optimization parameters have a good effect Platform: |
Size: 1024 |
Author:liwei |
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Description: Simulated Annealing (SA) is a smart (meta)-heuristic for Optimization. Given a cost function in a large search space, SA replaces the current solution by a random "nearby" solution. The nearby solution is chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (a.k.a the temperature). T is gradually decreased during the process. The current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima.
This approach has some similitude with Physic, where the heat causes the atoms to become unstuck from their initial positions and wander randomly through states of higher energy the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one.-Simulated Annealing (SA) is a smart (meta)-heuristic for Optimization. Given a cost function in a large search space, SA replaces the current solution by a random " nearby" solution. The nearby solution is chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (aka the temperature). T is gradually decreased during the process. The current solution changes almost randomly when T is large, but increasingly " downhill" as T goes to zero. The allowance for " uphill" moves saves the method from becoming stuck at local minima. This approach has some similitude with Physic, where the heat causes the atoms to become unstuck from their initial positions and wander randomly through states of higher energy the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. Platform: |
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Author:dingchong |
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Description: Optimization by simulated annealing by S. Kirkpatrick, C. D. Gelatt, Jr., M. P. Vecchi
13 May 1983, Volume 220, Number 4598 Platform: |
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Author:niloofar |
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Description: 模拟退火算法最早的思想由Metropolis等（1953）提出，1983年Kirkpatrick等将其应用于组合优化。-Simulated annealing algorithm was first thought by Metropolis et al (1953) suggested that, in 1983, Kirkpatrick and so on will be applied to combinatorial optimization. Platform: |
Size: 552960 |
Author:Kevin |
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Description: 求解双层规划问题常用的算法有极点算法、直接搜索法、下降法和非数值优化方法(如模拟退火算法、遗传算法等)，遗传算法的求解思路是：首先对上层的决策变量编码，代人下层规划模型，通过求解下层模型的决策变量值，代入上层模型计算适应度值，然后进行交叉、变异、选择操作，最后求出最优解。-Solving Bilevel Programming Problems with pole algorithm commonly used algorithms, direct search method, descent and non-numerical optimization methods (such as simulated annealing, genetic algorithms, etc.), genetic algorithm for solving approaches: the first on the top of the decision variable coding, on behalf of the were lower programming model, by solving the lower model of decision variables values into the upper model on behalf of the fitness value, and then crossover and mutation, select Options, and finally get the best solution. Platform: |
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Author:大海 |
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Description: 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 the search process by giving a time-change and ultimately tends to zero the probability of jumps, and thus can effectively avoid falling into local minima and ultimately tends to the global optimum of the serial structure of the optimization algorithm Platform: |
Size: 3072 |
Author:shitou |
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Description: TSP问题是一个典型的、容易描述但是难以处理的NP完全问题，同时TSP问题也是诸多领域内出现的多种复杂问题的集中概括和简化形式。目前求解TSP问题的主要方法有启发式搜索法、模拟退火算法、遗传算法、Hopfield神经网络算法、二叉树描述算法。所以，有效解决TSP问题在计算理论上和实际应用上都有很高的价值，而且TSP问题由于其典型性已经成为各种启发式的搜索、优化算法的间接比较标准(如遗传算法、神经网络优化、列表寻优(TABU)法、模拟退火法等)。遗传算法就其本质来说，主要是解决复杂问题的一种鲁棒性强的启发式随机搜索算法。因此遗传算法在TSP问题求解方面的应用研究，对于构造合适的遗传算法框架、建立有效的遗传操作以及有效地解决TSP问题等有着多方面的重要意义。-The TSP The problem is a typical, easy to describe but difficult to handle the NP-complete problem, the TSP many areas centralized summarized and simplified form of a variety of complex issues. The main method of solving TSP heuristic search method, simulated annealing, genetic algorithm, Hopfield neural network algorithm, the binary tree to describe the algorithm. Therefore, an effective solution to the TSP has a very high value in the calculation of the theoretical and practical applications, and TSP problem has become due to its typical variety of heuristic search, optimization of indirect comparison standard (such as genetic algorithms, neural networks optimization list optimization (TABU), simulated annealing, etc.). The genetic algorithm is by its very nature, a robustness to solve complex problems heuristic random search algorithm. Genetic algorithm TSP problem solving aspects of applied research, genetic algorithm framework for constructing a suitable, effective genetic manipul Platform: |
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Author:孟晓龙 |
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Description: Optimization by simulated annealing genetic algorithm, genetic algorithm so that the reverse search capabilities, through the simulation shows that can be better value. Platform: |
Size: 13312 |
Author:eaclicker |
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Description: Simulated annealing- particle swarm optimization, the program will be simulated annealing algorithm and particle swarm optimization by combining optimization parameters have a good effect Platform: |
Size: 1024 |
Author:eaclicker |
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Description: 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 by giving the search process and ultimately tends to zero the probability of jumps, thus can effectively avoid local minima and eventually become a global optimization algorithm optimal serial structure. Platform: |
Size: 65536 |
Author:殷建立 |
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Description: 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. Platform: |
Size: 7168 |
Author:May |
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Description: 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 Platform: |
Size: 4096 |
Author:翔子 |
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Description: 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 Platform: |
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Author:HMCFD |
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Search - Optimization by Simulated Annealing