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21.
Pure adaptive search constructs a sequence of points uniformly distributed within a corresponding sequence of nested regions of the feasible space. At any stage, the next point in the sequence is chosen uniformly distributed over the region of feasible space containing all points that are equal or superior in value to the previous points in the sequence. We show that for convex programs the number of iterations required to achieve a given accuracy of solution increases at most linearly in the dimension of the problem. This compares to exponential growth in iterations required for pure random search. 相似文献
22.
Analysis of Static Simulated Annealing Algorithms 总被引:1,自引:0,他引:1
Generalized hill climbing (GHC) algorithms provide a framework for modeling local search algorithms to address intractable discrete optimization problems. This paper introduces a measure for determining the expected number of iterations to visit a predetermined objective function level, given that an inferior objective function level has been reached in a finite number of iterations. A variation of simulated annealing (SA), termed static simulated annealing (S2A), is analyzed using this measure. S2A uses a fixed cooling schedule during the algorithm execution. Though S2A is probably nonconvergent, its finite-time performance can be assessed using the finite-time performance measure defined in this paper. 相似文献
23.
First, this paper deals with lagrangean heuristics for the 0-1 bidimensional knapsack problem. A projected subgradient algorithm is performed for solving a lagrangean dual of the problem, to improve the convergence of the classical subgradient algorithm. Secondly, a local search is introduced to improve the lower bound on the value of the biknapsack produced by lagrangean heuristics. Thirdly, a variable fixing phase is embedded in the process. Finally, the sequence of 0-1 one-dimensional knapsack instances obtained from the algorithm are solved by using reoptimization techniques in order to reduce the total computational time effort. Computational results are presented. 相似文献
24.
Pure adaptive search in global optimization 总被引:10,自引:0,他引:10
Pure adaptive seach iteratively constructs a sequence of interior points uniformly distributed within the corresponding sequence of nested improving regions of the feasible space. That is, at any iteration, the next point in the sequence is uniformly distributed over the region of feasible space containing all points that are strictly superior in value to the previous points in the sequence. The complexity of this algorithm is measured by the expected number of iterations required to achieve a given accuracy of solution. We show that for global mathematical programs satisfying the Lipschitz condition, its complexity increases at mostlinearly in the dimension of the problem.This work was supported in part by NATO grant 0119/89. 相似文献
25.
26.
时贞军 《数学物理学报(A辑)》2004,4(6):675-682
该文提出一种无约束优化非线性共轭梯度法,证明了精确线性 搜索下的全局收敛性。当目标函数为一致凸函数时,证明了算法具有线性收敛速度。数值实验表明算法对于求解实际问题是有效的。 相似文献
27.
Huang H. X. Liang Z. A. Pardalos P. M. 《Journal of Optimization Theory and Applications》2004,120(1):53-71
A flow search approach is presented in this paper. In the approach, each iterative process involves a subproblem, whose variables are the stepsize parameters. Every feasible solution of the subproblem corresponds to some serial search stages, the stepsize parameters in different search stages may interact mutually, and their optimal values are determined by evaluating the total effect of the interaction. The main idea of the flow search approach is illustrated via the minimization of a convex quadratic function. Based on the flow search approach, some properties of the m-step linear conjugate gradient algorithm are analyzed and new bounds on its convergence rate are also presented. Theoretical and numerical results indicate that the new bounds are better than the well-known ones. 相似文献
28.
Finding Robust Solutions Using Local Search 总被引:1,自引:0,他引:1
Kenneth Sörensen 《Journal of Mathematical Modelling and Algorithms》2004,3(1):89-103
This paper investigates how a local search metaheuristic for continuous optimisation can be adapted so that it finds broad
peaks, corresponding to robust solutions. This is relevant in problems in which uncertain or noisy data is present. When using
a genetic or evolutionary algorithm, it is standard practice to perturb solutions once before evaluating them, using noise
from a given distribution. This approach however, is not valid when using population-less techniques like local search and
other heuristics that use local search. For those algorithms to find robust solutions, each solution needs to be perturbed
and evaluated several times, and these evaluations need to be combined into a measure of robustness. In this paper, we examine
how many of these evaluations are needed to reliably find a robust solution. We also examine the effect of the parameters
of the noise distribution. Using a simple tabu search procedure, the proposed approach is tested on several functions found
in the literature.
This revised version was published online in August 2006 with corrections to the Cover Date. 相似文献
29.
Lufthansa Technical Training GmbH (LTT) performs training courses for Lufthansa Technik AG as well as for several other international airlines. Courses of about 670 different types are offered of which several hundred take place each year. The course scheduling problem faced by LTT is to construct a yearly schedule which maximizes the profit margin incurred while meeting a variety of complex precedence, temporal, and resource-related constraints. A “good” operational schedule should also meet a number of additional subordinate objectives. We formalize the problem and develop a heuristic scheme along with several priority rules, as well as a local search algorithm to determine well-suited weights for weighted composite rules. The operational planning situation of 1996 served as our major test instance; additional test instances were constructed by modifying this data. Several computational experiments were carried out to evaluate the performance of the algorithms. It turned out that the best so-found schedule is substantially better in terms of the profit margin incurred than the solution manually constructed by LTT. 相似文献
30.
Most successful heuristics for solving 1||∑wjTj are based on swap moves. We present an algorithm which improves the complexity of searching the swap neighborhood from O(n3) to O(n2). We show that this result also improves the complexity of the recently developed dynasearch heuristics. 相似文献