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1.
本文提出了一种求解约束优化问题的新算法—投影梯度型中心方法.在连续可微和非退化的假设条件下,证明了其全局收敛性.本文算法计算简单且形式灵活.  相似文献   

2.
一种改进的禁忌搜索算法及其在连续全局优化中的应用   总被引:2,自引:1,他引:1  
禁忌搜索算法是一种元启发式的全局优化算法,是局部搜索算法的一种推广,已被成功地应用于许多组合优化问题中。本文针对有界闭区域上的连续函数全局优化问题,提出了一种改进的禁忌搜索算法,并进行了理论分析和数值实验。数值实验表明,对于连续函数全局优化问题的求解该算法是可行有效的,并且结构简单,迭代次数较少,是一种较好的全局启发式优化算法。  相似文献   

3.
马昌凤  梁国平 《数学杂志》2004,24(4):399-402
提出了求解混合互补问题的一个光滑逼近算法,并在一定条件下证明了该算法的全局收敛性.  相似文献   

4.
本文给出了一个新的非线性约束优化的可行方向法.该算法适用于退化问题(积极约束梯度线性相关),算法结构简单,在适当条件下,证明此算法具有全局收敛性.数值实验表明算法是有效的.  相似文献   

5.
一个新的无约束优化超记忆梯度算法   总被引:3,自引:0,他引:3  
时贞军 《数学进展》2006,35(3):265-274
本文提出一种新的无约束优化超记忆梯度算法,算法利用当前点的负梯度和前一点的负梯度的线性组合为搜索方向,以精确线性搜索和Armijo搜索确定步长.在很弱的条件下证明了算法具有全局收敛性和线性收敛速度.因算法中避免了存贮和计算与目标函数相关的矩阵,故适于求解大型无约束优化问题.数值实验表明算法比一般的共轭梯度算法有效.  相似文献   

6.
一类非拟Newton算法及其收敛性   总被引:14,自引:0,他引:14  
本文对求解无约束最优化问题提出一类非拟Newton算法,此方法同样具有二次终止性,产生的矩阵序列保持正定对称传递性,并证明了新类中的任何一种算法的全局收敛和超线性收敛性。  相似文献   

7.
演化策略的全局收敛性   总被引:23,自引:1,他引:22  
郭崇慧  唐焕文 《计算数学》2001,23(1):105-110
1.引言 进化算法(EA, Evolutionary Algorithms)是近年来兴起的一类基于生物界的自然选择和自然遗传机制的计算方法,如遗传算法(GA, Genetic Algorithms)、演化策略(ES,Evolution Strategies)和进化规划(EP, Evolutionary Programming)等方法.这类算法的主要优点在于其本质上的并行性、广泛的可应用性和算法的高度稳健性、简明性与全局优化性[1,2].目前,进化算法已被广泛地应用于计算机科学、工程技术、管理科学和社会科…  相似文献   

8.
本文研究了大规模无约束优化问题,利用BFGS逼近搜索方向,提出了两种关于HSDY方法的自适应共轭梯度算法(HSDY1和HSDY2).新算法具有充分下降性和全局收敛性.数值实验表明,新方法比HSDY的计算性能更优.  相似文献   

9.
结合一种新搜索的Broyden算法类的全局收敛性   总被引:1,自引:0,他引:1  
本文提出了一种与回追搜索(backtrackinglinesearch)有关的可行线性搜索.在通常的条件下,证明了结合这一新的搜索的Broyden算法类具有全局收敛性.  相似文献   

10.
求解变量带简单界约束的非线性规划问题的信赖域方法   总被引:3,自引:0,他引:3  
陈中文  韩继业 《计算数学》1997,19(3):257-266
1.引言。本文考虑下述变量带简单界约束的非线性规划问题:问题(1.1)不仅是实际应用中出现的简单的约束最优化问题,而且相当一部分最优化问题可以把变量限制在有意义的区间内181.因此,无论在理论方面还是在实际应用方面,都有必要研究此种问题.给出简便而且有效的算法.有些文章提出了一些特殊的方法.如011和[2].14]及16]提出了一类信赖域方法,它们都借助于某种辅助点,证明了算法的全局收敛性.在收敛速度的分析方面,除要求在*-T点满足严格互补松弛外,它们还要求另一个条件,即在每次迭代中,辅助点的有效约束必须在尝…  相似文献   

11.
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.  相似文献   

12.
A comparison of local search methods for flow shop scheduling   总被引:1,自引:0,他引:1  
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.  相似文献   

13.
In this paper, we propose a new kind of simulated annealing algorithm calledtwo-level simulated annealing for solving certain class of hard combinatorial optimization problems. This two-level simulated annealing algorithm is less likely to get stuck at a non-global minimizer than conventional simulated annealing algorithms. We also propose a parallel version of our two-level simulated annealing algorithm and discuss its efficiency. This new technique is then applied to the Molecular Conformation problem in 3 dimensional Euclidean space. Extensive computational results on Thinking Machines CM-5 are presented. With the full Lennard-Jones potential function, we were able to get satisfactory results for problems for cluster sizes as large as 100,000. A peak rate of over 0.8 giga flop per second in 64-bit operations was sustained on a partition with 512 processing elements. To the best of our knowledge, ground states of Lennard-Jones clusters of size as large as these have never been reported before.Also a researcher at the Army High Performance Computing Research Center, University of Minnesota, Minneapolis, MN 55415  相似文献   

14.
Two common questions when one uses a stochastic global optimization algorithm, e.g., simulated annealing, are when to stop a single run of the algorithm, and whether to restart with a new run or terminate the entire algorithm. In this paper, we develop a stopping and restarting strategy that considers tradeoffs between the computational effort and the probability of obtaining the global optimum. The analysis is based on a stochastic process called Hesitant Adaptive Search with Power-Law Improvement Distribution (HASPLID). HASPLID models the behavior of stochastic optimization algorithms, and motivates an implementable framework, Dynamic Multistart Sequential Search (DMSS). We demonstrate here the practicality of DMSS by using it to govern the application of a simple local search heuristic on three test problems from the global optimization literature.  相似文献   

15.
汤丹 《运筹学学报》2011,15(4):124-128
本文是对非线性规划问题提出的一种算法,该算法把模拟退火算法应用到CRS算法中,根据模拟退火算法每一次迭代都体现集中和扩散两个策略的平衡的特点,使CRS算法更能够搜索到全局最优解,而不会陷入局部最优解。最后把提出的算法应用到两个典型的函数优化问题中,结果表明,算法是可行的、有效的  相似文献   

16.
The general facility location problem and its variants, including most location-allocation and P-median problems, are known to be NP-hard combinatorial optimization problems. Consequently, there is now a substantial body of literature on heuristic algorithms for a variety of location problems, among which can be found several versions of the well-known simulated annealing algorithm. This paper presents an optimization paradigm that, like simulated annealing, is based on a particle physics analogy but is markedly different from simulated annealing. Two heuristics based on this paradigm are presented and compared to simulated annealing for a capacitated facility location problem on Euclidean graphs. Experimental results based on randomly generated graphs suggest that one of the heuristics outperforms simulated annealing both in cost minimization as well as execution time. The particular version of location problem considered here, a location-allocation problem, involves determining locations and associated regions for a fixed number of facilities when the region sizes are given. Intended applications of this work include location problems with congestion costs as well as graph and network partitioning problems.  相似文献   

17.
A derivative-free simulated annealing driven multi-start algorithm for continuous global optimization is presented. We first propose a trial point generation scheme in continuous simulated annealing which eliminates the need for the gradient-based trial point generation. We then suitably embed the multi-start procedure within the simulated annealing algorithm. We modify the derivative-free pattern search method and use it as the local search in the multi-start procedure. We study the convergence properties of the algorithm and test its performance on a set of 50 problems. Numerical results are presented which show the robustness of the algorithm. Numerical comparisons with a gradient-based simulated annealing algorithm and three population-based global optimization algorithms show that the new algorithm could offer a reasonable alternative to many currently available global optimization algorithms, specially for problems requiring ‘direct search’ type algorithm.  相似文献   

18.
《Optimization》2012,61(4):1057-1080
In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.  相似文献   

19.
In this paper, a computational algorithm, named RST2ANU algorithm, has been developed for solving integer and mixed integer global optimization problems. This algorithm, which primarily is based on the original controlled random search approach of Price [22i], incorporates a simulated annealing type acceptance criterion in its working so that not only downhill moves but also occasional uphill moves can be accepted. In its working it employs a special truncation procedure which not only ensures that the integer restrictions imposed on the decision variables are satisfied, but also creates greater possibilities for the search leading to a global optimal solution. The reliability and efficiency of the proposed RST2ANU algorithm has been demonstrated on thirty integer and mixed integer optimization problems taken from the literature. The performance of the algorithm has been compared with the performance of the corresponding purely controlled random search based algorithm as well as the standard simulated annealing algorithm. The performance of the method on mathematical models of three realistic problems has also been demonstrated.  相似文献   

20.
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 (TC). 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.  相似文献   

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