模拟退火算法

免疫模拟退火算法及其在柔性动态Jobshop中的应用

Immune Simulated Annealing Hybrid Algorithm and Its Application for Flexible Dynamical Job Shop Scheduling

基于模拟退火算法的BP网络在水文水资源中应用

The BP Network Based on Simulated Annealing Algorithm and Application in Hydrology

DNA分类方法的探讨基于极快速模拟退火算法的地层横波各向异性反演

On Classification of DNA Computing method of shear wave anisotropy of formation based on very fast simulated reannealing

广义Gauss模型及其模拟退火算法

Extended Gauss Model and Its Simulated Annealing Algorithm

利用模拟退火算法，得到使参与粗交流的各个agent获得信息量最大的粗交流传递序列。

In order to get the translation sequence , in which every agent taking part in rough communication gets maximum information , simulated annealing algorithm is used .

基于并行模拟退火算法的TSP问题求解

Solving the TSP by Parallel Simulated Annealing Algorithm

将模拟退火算法应用在CBD系统的实例调整。

This paper applies simulated annealing algorithm to case adaptation .

结合创成式CAPP系统中工步优化问题，介绍了由遗传算法（GeneticAlgorithms，GA）和模拟退火算法（SimulatedAnnealingAlgorithms，SA）构成的混合寻优策略。

In this paper , the problem of operation optimization in generative CAPP is discussed . A mixed strategy for operation optimization using genetic algorithms and simulated annealing algorithms is introduced .

模拟退火算法（SimulatedAnnealingalgorithm，SA）来源于固体退火原理，是一种通用概率算法，用来在一个大的搜寻空间内寻找问题的最优解。

Simulated annealing algorithm ( SA ), derived from the principle of solid annealing , is a general probabilistic algorithm and used to find the optimal solution of the problem in large search space .

在线参数的方法包括：模拟退火算法（SA）、粒子群算法（PSO）、单纯形法和强化学习方法等等。

The methods of online learning parameters include : Simulated Annealing ( SA ), Particle Swarm Optimization ( PSO ), the Simplex and Reinforcement Learning method .

模拟退火算法是目前发展较快的智能优化算法，是一种以概率l收敛于全局最优解的全局优化算法。

Simulated annealing is an intelligent algorithm of developing very fast .

本文提出用遗传&模拟退火算法（GASA）混合优化策略来求解双层规划模型。

The author proposes GASA hybrid optimization strategy for the bilevel programming models .

实施了最优保留策略，改进交叉和变异操作，并结合模拟退火算法（SA）的Metropolis判别准则的复制策略，使寻优过程能够跳出局部最优解，从而形成了混合遗传算法。

The hybrid genetic algorithm is formed combined with the Metropolis discrimination criteria of simulated annealing algorithm ( SA ), which can jump from local optimization solution .

根据吉布斯马尔可夫随机场模型和SAR图像斑点噪声的伽玛分布统计特性，应用模拟退火算法，实现雷达截面的全局优化伪似然估计。

Based on Gibbs-Markov random field model and the Gamma distribution property of SAR image 's speckle noise , global optimal pseudo likelihood estimation of radar cross-section can be achieved with simulated annealing algorithm .

混合SPMD模拟退火算法及其应用

Hybrid SPMD Simulated Annealing Algorithm and Its Applications

但是，BP算法训练神经网络速度慢、易陷入局部极值。而模拟退火算法（SA）具有很好的全局寻优性。

However , the BP algorithm provides a slow training toNN and is easy to fall into local extremum while the Simulant Anneal Algorithm ( SA ) gives a good performance in overall optimization searching .

基于改进的模拟退火算法和边坡稳定计算的简化Bishop算法，建立了边坡稳定分析最小安全系数全局搜索方法。

Based on the modified annealing simulation and simplified Bishop method for computing slope stability , an effective global minimization algorithm is presented for searching the minimal safety factor .

退火策略是模拟退火算法中的重要一环，本文将研究退火策略对模拟退火算法的影响问题，给出了MATLAB语言程序，典型复杂函数优化的仿真表明退火速率应适中。

The effect of annealing strategy in simulated annealing algorithm is researched in this paper . MATLAB programs of simulated annealing algorithm are given . Simulation results of typical complex function optimization show that the speed of annealing must be moderate .

本文在基本微粒群算法（PSO）的位置更新中引入了模拟退火算法思想，并改进了模拟退火算法（SA）中的降温操作。

This paper introduces the concept of simulated annealing algorithm ( SA ) in the position modifying of the particle swarm optimization algorithm ( PSO ) and improve the operation on dropping in temperature .

由仿真计算和实例分析可以看出，与传统优化算法相比，这种混沌模拟退火算法（CSA）能更为快速可靠地收敛到全局最优解，在求解脑电逆问题上具有更高的适应性和可行性。

Compared with the traditional stochastic optimization algorithm , CSA converged more quickly and accurately to the global minima and proved a promise global optimization method of high adaptability and feasibility .

本文在Hopfield神经网络优化方法的基础上，根据模拟退火算法逃离局部最优解的原理，提出了一种神经网络优化计算的新方法。

According to the principle of escaping from local optimal solutions of simulated annealing algorithm , a new neural computing method for optimization is proposed based on Hopfield neural network method .

本文对已经提出的各种解决旅行商问题的方法进行了比较，这些算法包括利用Hopfield神经网络，遗传算法，模拟退火算法。

There are different methods to solve it and in this paper , we compared these method such as Hopfield network , GA algorithm , anneal simulating algorithm and so on .

介绍了Metropolis准则，给出了模拟退火算法解决生产调度问题的基本方法和步骤，并对算法的有效性进行了验证。

We proposed in this paper the Metropolis rule and gave the basic ways and steps to solve scheduling problem with Simulated Annealing Algorithm . Furthermore , we verified the validity of the algorithm .

实验结果表明，即使当各个传感FBG的光谱已经发生部分重叠或者完全重叠，利用光谱形状复用技术和模拟退火算法依然能够解调出其各自的中心波长。

Experimental results show that spectral shape multiplexing technique and the improved simulated annealing algorithm can still demodulate the center wavelengths even when the spectrum of one FBG partly or wholly overlaps with others ' .

主要利用连续型Hopfield人工神经网络进行了设备布置问题的优化求解计算.把模拟退火算法引入计算机优化搜索过程，有效地提高了设备布置方案的优化效果。

Hopfield neural network is applied in the facility layout problem ( FLP ) . By using the simulated annealing algorithms to search the optimal facility layout , The layout result is improved effectively .

本文用模糊数表示需求的不确定性，提出了一种采用连续盘点（Q，r）库存控制策略的模糊库存模型，并利用模拟退火算法求解最佳订货点和最优订货批量。

In this paper , uncertain demand is represented by fuzzy numbers and a continuous review ( Q , r ) inventory model is developed . The simulated annealing algorithm is used to solve the optimal reorder level and order quantity of the models .

这里通过分析模拟退火算法和遗传算法的特性，将两种算法去粕取精，引出GASA混合算法。

We can educe GASA combination algorithms by analyze the characteristics of simulated algorithms and genetic algorithms and absorb their elite and eliminate scum .

将禁忌搜索算法与免疫进化算法、模拟退火算法有机地结合起来，建立了求解优化问题的混合禁忌搜索算法（HTS）。

The article presents Hybrid Taboo Search ( HTS ) algorithm in order to solve optimizing problem well . Hybrid Taboo Search is set up through combining Taboo Search with Simulated Anneal and Immune Arithmetic .

定位系统中，通过将雷达的位置估计和图像的灰度信息进行融合来获得位移参数，并将改进的Hausdorff距离和模拟退火算法相结合得到旋转参数。

Translation parameter of vehicle location is obtained by fusing radar-based position evaluation data and image intensity data , and rotation parameter of location is obtained by integrating improved Hausdorff distance and simulated annealing algorithm .

由于样本中的历史需水资料规律不明显，用BP网络往往不收敛，如果想要达到与遗传模拟退火算法的训练方法相同的效果，则要花更多的时间进行训练。

As the sample water demand information on the history of the law is not obvious , using BP networks often do not converge . To achieve and genetic simulated annealing algorithm for the same effect as the training method , there have to spend more time training .