基于动态BP网络误差修正的广义预测控制

针对建模误差对非线性系统预测控制鲁棒性的影响 ,提出了一种基于动态 BP网络的广义预测控制算法 .该算法运用动态 BP网络对模型预测误差进行在线补偿 ,以提高预测精度 .仿真结果证明了本文提出的广义预测控制算法对于非线性系统是有效的     

基于模拟退火算法的船用核动力装置故障诊断研究

针对核动力装置故障诊断存在的诊断精度低等问题,提出了一种基于模拟退火算法和概率因果模型相结合的故障诊断方法.首先根据故障样本集和概率因果理论建立动态多故障诊断模型,将复杂系统的多故障诊断转换成非线性规划问题.利用模拟退火算法对该问题进行求解,并建立了诊断测试系统.测试结果表明,方法能有效避免误诊、漏诊现象,可用于复杂核动力装置的动态多故障诊断.     

基于模拟退火算法的船用核动力装置故障诊断研究北大核心

针对核动力装置故障诊断存在的诊断精度低等问题,提出了一种基于模拟退火算法和概率因果模型相结合的故障诊断方法.首先根据故障样本集和概率因果理论建立动态多故障诊断模型,将复杂系统的多故障诊断转换成非线性规划问题.利用模拟退火算法对该问题进行求解,并建立了诊断测试系统.测试结果表明,方法能有效避免误诊、漏诊现象,可用于复杂核动力装置的动态多故障诊断.     

免疫算法在蛋白质折叠模拟中的应用

利用免疫算法．结合非格模型．对于长度为12～20的氨基酸序列的折叠进行预测，并与标准的遗传算法和模拟退火算法进行对比，该算法有更强的全局搜索能力，对减轻遗传算法后期波动性有明显效果，同时使收敛的速度有所提高．     

基于模拟退火地震反演技术——以X油田某区为例

为了提高地震反演预测的分辨率和可信度,提出了线性反演与非线性反演二者相结合的反演方法——以稀疏脉冲反演结果为约束背景的基于模拟退火的反演方法,阐述了基于模拟退火法的反演机理,并以X油田某区为例,开展了基于模拟退火地球物理反演预测,从反演分辨率、可信度和误差三个方面进行分析和定量研究.结果表明,非线性的随机反演与线性反演相结合有效地提高了反演分辨率,纵向上能够精细到单砂体级,反演结果多个概率的实现最大程度上降低反演的多解性,并且,反演结果的精度较高,2m以上砂岩反演符合率均在90%以上.     

基于SA-GA混合算法的动车组车辆轮重分配优化

针对动车组车辆出厂前存在的轮重偏差问题,建立动车组车辆轮重调节力学模型,利用模拟退火算法（SA）的机制和遗传算法（GA）非均匀变异思想,提出了一种模拟退火遗传（SA-GA）混合算法,并利用该混合算法对车辆轮重调节力学模型进行数值求解, 结果显示: 轮重偏差降低到1.2%以下,符合GB/T 3317—2006的规定.同时使用SIMPACK软件仿真,将该仿真结果与数值计算结果对比分析,结果显示:基于SA GA混合算法的计算结果是正确的,这为快速优化轮重分配结果提供了一种有效的计算方法.     

应用PSO-改进GM(1,1)模型预测油田产量

准确预测油田产量对油田开发调整部署有重要意义.依据油田产量变化特征与油田产量数据丰富的特点,引入产量变化系数修正传统灰色模型,并运用中心差商改进了传统灰导数离散过程,减小灰导数离散误差,再结合PSO算法,最终建立了PSO-改进GM(1,1)模型.运用PSO-改进GM(1,1)模型进行油田产量预测,对比传统灰色模型与PSO-改进GM(1,1)模型的产量预测结果可知,与传统灰色模型预测结果相比,应用PSO-改进GM(1,1)模型进行油田产量预测具有更高的准确性.     

果蝇优化最小二乘支持向量机混合预测模型—以我国物流需求量预测为例

为解决最小二乘支持向量机参数设置的盲目性,利用果蝇优化算法对其参数进行优化选择,进而构建了果蝇优化最小二乘支持向量机混合预测模型.以我国物流需求量预测为例,验证了该模型的可行性和有效性.实例验证结果表明:与单一最小二乘支持向量机和模拟退火算法优化最小二乘支持向量机预测模型相比,该模型不仅能够有效选择参数值,而且预测精度更高.     

基于AdaBoost-SVM算法的油井反向压驱效果预测方法

预测油井的反向压驱增油效果是反向压驱选井选层工作的重要环节.针对反向压驱历史数据规模有限的问题,提出一种基于AdaBoost-SVM的油井反向压驱增油效果预测方法.应用SMOTE算法对压驱历史数据进行过采样,并建立AdaBoost-SVM反向压驱增油效果预测模型,以对油井的压驱效果进行预测.在油井上的实际应用结果表明,该预测方法能够有效预测油井应用反向压驱工艺的增油效果,为进一步的选井选层工作提供参考.     

时变条件下带时间窗车辆调度问题的模拟退火算法

带时间窗车辆调度问题(VRPTW)是一类要求满足容积和时间窗约束的车辆路径优化问题,现 有大部分相关文献只考虑了车辆行驶速度恒定的情况,忽略了各种动态因素的影响.本文研究的时变条件下带时间窗车辆调度问题将车辆行驶速度考虑成时变分段函数,并利用模拟退火算法进行求解,最后通过实验结果说明算法的有效性.     

Solving Location-allocation Problems with Rectilinear Distances by Simulated Annealing

The objective of this study is to use the simulated annealing method to solve minisum location-allocation problems with rectilinear distances. The major advantage of the simulated annealing method is that it is a very general and efficient algorithm for solving combinatorial optimization problems with know objective functions. In this study, a simulated annealing algorithm was developed to solve the location-allocation problems, and its performance was compared with two other popular methods for solving location-allocation problems. The results show that simulated annealing is a good alternative to the two methods, as measured by both the solution quality and the computational time.     

A comparison of local search methods for flow shop scheduling

Local search techniques are widely used to obtain approximate solutions to a variety of combinatorial optimization problems. Two important categories of local search methods are neighbourhood search and genetic algorithms. Commonly used neighbourhood search methods include descent, threshold accepting, simulated annealing and tabu search. In this paper, we present a computational study that compares these four neighbourhood search methods, a genetic algorithm, and a hybrid method in which descent is incorporated into the genetic algorithm. The performance of these six local search methods is evaluated on the problem of scheduling jobs in a permutation flow shop to minimize the total weighted completion time. Based on the results of extensive computational tests, simulated annealing is found to generate better quality solutions than the other neighborhood search methods. However, the results also indicate that the hybrid genetic descent algorithm is superior to simulated annealing.     

Cybernetic optimization by simulated annealing: Accelerating convergence by parallel processing and probabilistic feedback control

The convergence of the simulated annealing algorithm is accelerated by a probabilistic feedback control scheme. This scheme uses two or more parallel processors to solve the same or related combinatorial optimization problems and are coupled by a probabilistic measure of quality (PMQ). The PMQ is used to generate an error signal for use in feedback control. Control over the search process is achieved by using the error signal to modulate the temperature parameter. Other aspects of control theory, such as the system gain and its effects on system performance, are described. Theoretical and experimental results show that such a scheme increases the steadystate probability of the globally optimal solutions.     

Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods

In this paper the usage of a stochastic optimization algorithm as a model search tool is proposed for the Bayesian variable selection problem in generalized linear models. Combining aspects of three well known stochastic optimization algorithms, namely, simulated annealing, genetic algorithm and tabu search, a powerful model search algorithm is produced. After choosing suitable priors, the posterior model probability is used as a criterion function for the algorithm; in cases when it is not analytically tractable Laplace approximation is used. The proposed algorithm is illustrated on normal linear and logistic regression models, for simulated and real-life examples, and it is shown that, with a very low computational cost, it achieves improved performance when compared with popular MCMC algorithms, such as the MCMC model composition, as well as with “vanilla” versions of simulated annealing, genetic algorithm and tabu search.     

Investigation of temperature parallel simulated annealing for optimizing continuous functions with application to hyperspectral tomography

The simulated annealing (SA) algorithm is a well-established optimization technique which has found applications in many research areas. However, the SA algorithm is limited in its application due to the high computational cost and the difficulties in determining the annealing schedule. This paper demonstrates that the temperature parallel simulated annealing (TPSA) algorithm, a parallel implementation of the SA algorithm, shows great promise to overcome these limitations when applied to continuous functions. The TPSA algorithm greatly reduces the computational time due to its parallel nature, and avoids the determination of the annealing schedule by fixing the temperatures during the annealing process. The main contributions of this paper are threefold. First, this paper explains a simple and effective way to determine the temperatures by applying the concept of critical temperature (T_C). Second, this paper presents systematic tests of the TPSA algorithm on various continuous functions, demonstrating comparable performance as well-established sequential SA algorithms. Third, this paper demonstrates the application of the TPSA algorithm on a difficult practical inverse problem, namely the hyperspectral tomography problem. The results and conclusions presented in this work provide are expected to be useful for the further development and expanded applications of the TPSA algorithm.     

A comparison of two methods for solving 0–1 integer programs using a general purpose simulated annealing algorithm

0–1 problems are often difficult to solve. Although special purpose algorithms (exact as well as heuristic) exist for solving particular problem classes or problem instances, there are few general purpose algorithms for solving practical-sized instances of 0–1 problems. This paper deals with a general purpose heuristic algorithm for 0–1 problems. In this paper, we compare two methods based on simulated annealing for solving general 0–1 integer programming problems. The two methods differe in the scheme used for neighbourhood transitions in the simulated annealing framework. We compare the performance of the two methods on the set partitioning problem.     

Image Restoration Using Modifications of Simulated Annealing

Abstract

This article uses a modified version of the simulated annealing algorithm to restore degraded spatial patterns. Standard simulated annealing is used to find an image that is a posterior mode when the number of images under consideration precludes sequential search for a maximum. I incorporate jumping probabilities of the annealing algorithm without randomization. The convergence of our algorithm is proven under a practical annealing schedule. The same idea is also implemented to improve the performance of other modifications of simulated annealing. These include forcing proportions of labels in an image, using posterior marginals, and incorporating an edge process. This article also studies nonlinear presmoothing of the observations.     

A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling

In this study, a general framework is proposed that combines the distinctive features of three well-known approaches: the adaptive memory programming, the simulated annealing, and the tabu search methods. Four variants of a heuristic based on this framework are developed and presented. The performance of the proposed methods is evaluated and compared with a conventional simulated annealing approach using benchmark problems for job shop scheduling. The unique feature of the proposed framework is the use of two short-term memories. The first memory temporarily prevents further changes in the configuration of a provisional solution by maintaining the presence of good elements of such solutions. The purpose of the second memory is to keep track of good solutions found during an iteration, so that the best of these can be used as the starting point in a subsequent iteration. Our computational results for the job shop scheduling problem clearly indicate that the proposed methods significantly outperform the conventional simulated annealing.     

Solving a dynamic cell formation problem using metaheuristics

In this paper, solving a cell formation (CF) problem in dynamic condition is going to be discussed by using some traditional metaheuristic methods such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Most of previous researches were done under the static condition. Due to the fact that CF is a NP-hard problem, then solving the model using classical optimization methods needs a long computational time. In this research, a nonlinear integer model of CF is first given and then solved by GA, SA and TS. Then, the results are compared with the optimal solution and the efficiency of the proposed algorithms is discussed.     

Improved methods of assembly sequence determination for automatic assembly systems

The assembly sequence is determined using product unit cost as the performance measure. The global optimum for a given problem is found by partially enumerating assembly station configurations with branch and bound methods. The study shows that the proposed methods perform faster than simulated annealing for the example problems used. It is shown that the unit cost function is not necessarily convex which is assumed in previous research.