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tabu search algorithms
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    Genetic algorithms and tabu search algorithms are two representative metaheuristic algorithms, based on which, we constructed an improved genetic algorithm to solve logistic vehicle routing problem, whose improvement is mainly presented at two aspects: on one hand, as tabu mutation operator, tabu search algorithm is inserted into genetic algorithm, which makes the two algorithms bringing out the best in each other.
    基于这两种算法,我们构造一个改进遗传算法厂仙人学M卜论义 配送中。 O车辆调度问题的叭九 柄公来解决配送车问题,其改进之处主要体现于两方面:第一,我们把t 禁忌搜索嵌入遗传算法中,作为遗传算法的禁忌变异算子,使这两种算法可以互相补充,相得益彰。
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  tabu search algorithms
Tabu search algorithms are developed for solving a large class of cyclic machine scheduling problems with the objective to minimize the cycle time.
      
Tabu Search Algorithms for Cyclic Machine Scheduling Problems
      
Cutting Plane/Tabu Search Algorithms for Low Rank Concave Quadratic Programming Problems
      
We then exploit these properties to derive four tabu search algorithms, each based on a different neighborhood.
      
Using a simple multiprocessor scheduling problem as a vehicle, we explore the behavior of tabu search algorithms using different tabu, local search and list management strategies.
      
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The Hough transformation has been a widely used in geometric primitive extraction.It relies basically on an evidence accumulation process.Recently a new family of techniques,namely the optimization based one was proposed which resorts mainly to repeated cost function evaluation process.The randomized Hough transformation and the Tabu search algorithm are the two good representatives of the the Hough family and optimization based family respectively.Although some piecemeal works exist,a systematic comparison...

The Hough transformation has been a widely used in geometric primitive extraction.It relies basically on an evidence accumulation process.Recently a new family of techniques,namely the optimization based one was proposed which resorts mainly to repeated cost function evaluation process.The randomized Hough transformation and the Tabu search algorithm are the two good representatives of the the Hough family and optimization based family respectively.Although some piecemeal works exist,a systematic comparison between these two families of techniques is unavailable in the literature.In this paper,based on a reasonable criterion,namely the expected number of random samples of minimum subset for a single successful primitive extraction,the performance of between the randomized Hough transform and the Tabu search algorithm is compared.The paper shows that the randomized Hough transformation generally outperforms the Tabu search algorithm.In particular,based on a large number of simulations and experiments with real images,it shows that with a comparable performance,the randomized Hough transformation is about twice as fast as the Tabu search algorithm for both line extraction and circle extraction.

Hough变换(HT)是目前应用最广的几何基元提取方法,其基本思想在于通过证据积累来提取基元.最近不少人又提出了通过代价函数的全局优化来提取几何基元的思想.随机Hough变换(RHT)和Tabu搜索(TS)分别是Hough变换和优化方法中的佼佼者.RHT和TS分别基于不同的策略,两种方法的相互比较在许多文献中已有提及,但目前尚无较完整的理论分析和系统的比较.本文在提取单个基元所需对最小点集的采样次数的期望值这一准则的基础上,对两种方法进行了比较.分析表明,随机Hough变换通常要优于Tabu搜索.从我们做的大量仿真和实际图像的实验也可看出,在提取的基元正确率相同的情况下,RHT约比TS快1—2倍.

A method is presented based on heuristics and tabu search algorithms, which can solve scheduling problem of mission path planning with different priority and limit resource. Satisfying result can acquire by dividing the missions in different layers according to their priority and scheduling in these layers. The scheduling algorithm has the advantage of small searching space and high solving speed. The simulation results show the efficiency of the algorithm.

基于启发式搜索和禁忌搜索技术 ,提出一种用于解决有限资源、不同重要性要求的任务路径规划问题的有效算法 ,通过对不同重要程度的任务进行分层调度 ,得到较为满意的决策结果。该算法具有搜索空间小、求解速度快的优点。仿真结果验证了算法的有效性

The automatic optimization of enterprise process model parameters is an optimization problem of the objectives and parameters system. This problem can be changed into the issue solving only one objective and parameters the system optimization with by linearity weighting method. Based on it, an automatic optimization technique in enterprise process modeling based on computer simulation is discussed in this paper, which combines FR (fletcher-reeves) method and TS (tabu-search) algorithm. The...

The automatic optimization of enterprise process model parameters is an optimization problem of the objectives and parameters system. This problem can be changed into the issue solving only one objective and parameters the system optimization with by linearity weighting method. Based on it, an automatic optimization technique in enterprise process modeling based on computer simulation is discussed in this paper, which combines FR (fletcher-reeves) method and TS (tabu-search) algorithm. The technique uses FR method to obtain the local optimization solutions, and uses TS algorithm to discover the global optimum in the sense of probability. The concept of tabu-area table is firstly proposed, and the tabu-list is improved from the two dimensions array to one dimension array, which uses to record the trace of optimize of the process model. FR method is improved by introducing search direction determining while near to the local optimization solutions so as to solve the peak and zigzag curve problem. The new technique can be generalized for model parameters optimization in the arbitrary curves, and be instructive for enterprise to implement BPR (business process reengineering) and ERP (enterprise resource planning) management.

企业过程模型参数自动优化是一个多参数多目标的系统优化问题.采用线性加权法将其转换成多参数单目标问题的求解,提出基于计算机模拟的企业过程模型参数的自动寻优方法.它将传统的共轭梯度法FR(fletcher reeves)和禁忌搜索算法TS(tabu search)结合起来,采用FR法进行局部寻优,由TS法实现从当前局部最优点向全域范围内的更优区域转移,循环往复达到求出全域范围最优点的目的.改进了FR法与TS法,克服了各自的缺陷,并提出禁忌区域表的概念,从而加速寻优过程.它适用于任意多维曲面的多极值问题最优求解,对企业进行BPR(business process reengineering)和实施ERP(enterprise resource planning)管理有较大的指导意义.

 
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