A Study on Optimizing the Double-Spine Type Flow Path Design for the Overhead Transportation System Using Tabu Search Algorithm

Optimizing Flow Path Design(FPD)is a popular research area in transportation system design,but its application to Overhead Transportation Systems(OTSs)has been limited.This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows.We employ transportation methods,specifically the North-West Corner and Stepping-Stone methods,to determine empty vehicle travel flows.Additionally,the Tabu Search(TS)algorithm is applied to branch the 10 stations into two main layout branches.The results obtained from our proposed method demonstrate a reduction in the objective function value compared to the initial feasible solution.Furthermore,we explore howchanges in the parameters of the TS algorithm affect the optimal result.We validate the feasibility of our approach by comparing it with relevant literature and conducting additional tests on layouts with 20 and 30 stations.

Tabu Search Implementation on Traveling Salesman Problem and Its Variations: A Literature Survey

The Traveling Salesman Problem (TSP) and its allied problems like Vehicle Routing Problem (VRP) are one of the most widely studied problems in combinatorial optimization. It has long been known to be NP-hard and hence research on developing algorithms for the TSP has focused on approximate methods in addition to exact methods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. In this paper, we review the tabu search literature on the TSP and its variations, point out trends in it, and bring out some interesting research gaps in this literature.

The P-Median Problem： A Tabu Search Approximation Proposal Applied to Districts

P-median is one of the most important Location-Allocation problems. This problem determines the location of facilities and assigns demand points to them. The p-median problem can be established as a discrete problem in graph terms as： Let G = （V, E） be an undirected graph where V is the set of n vertices and E is the set of edges with an associated weight that can be the distance between the vertices dij= d（vi, Vj） for every i, j =1,...,n in accordance to the determined metric, with the distances a symmetric matrix is formed, finding Vp∈ V such that ｜ Vp｜∈ = p, where p can be either variable or fixed, and the sum of the shortest distances from the vertices in {V-Vp} to their closet vertex in Vp is reduced to the minimum. Under these conditions the P-median problem is a combinatory optimization problem that belongs to the NP-hard class and the approximation methods have been of great aid in recent years because of this. In this point, we have chosen data from OR-Library [1] and we have tested three algorithms that have given good results for geographical data （Simulated Annealing, Variable Neighborhood Search, Bioinspired Variable Neighborhood Search and a Tabu Search-VNS Hybrid （TS-VNS）. However, the partitioning method PAM （Partitioning Around Medoids）, that is modeled like the P-median, attained similar results along with TS-VNS but better results than the other metaheuristics for the OR-Library instances, in a favorable computing time, however for bigger instances that represent real states in Mexico, TS-VNS has surpassed PAM in time and quality in all instances. In this work we expose the behavior of these five different algorithms for the test matrices from OR-Library and real geographical data from Mexico. Furthermore, we made an analysis with the goal of explaining the quality of the results obtained to conclude that PAM behaves with efficiency for the OR-Library instances but is overcome by the hybrid when applied to real instances. On the other hand we have tested the 2 best algorithms （PAM and TS-VNS） with geographic data geographic from Jalisco, Queretaro and Nuevo Leon. In this point, as we said before, their performance was different than the OR-Library tests. The algorithm that attains the best results is TS-VNS.

基于GA-TS搜索算法的工业无线传感网节点定位研究

为了解决工业无线传感网覆盖过程中通信半径跳距离产生偏差过大的问题,设计了一种基于GA-TS搜索算法的节点定位方法,并开展仿真分析。研究结果表明,当通信半径不断增大后,算法误差产生不同程度的减少。与传统最大似然估计定位算法相比,GA-TS搜索算法的优化效果更佳,误差大概缩减30%;与移动锚节点算法相比,误差大概降低至15%。相比较之下GA-TS搜索算法定位精度误差最小,具有很大的应用准确性和适应范围。该研究有助于工业无线传感网定位精度,对保证网络信号的传输效率具有一定的意义。

Tabu Search集中性和多样性自动平衡下的增强搜索策略

在禁忌搜索算法中,集中性搜索与多样性搜索是缺一不可但又相互矛盾的两个方面。本文提出了一种在禁忌搜索集中性和多样性自动平衡下的增强搜索策略算法,这种算法在集中性搜索与多样性搜索之间保持合理平衡的同时,又进一步对结果加强集中性搜索或者多样性搜索,以获全局最优解。以组合优化中的典型难题 TSP为例,通过自动更换邻域、候选集,较好地解决了集中性搜索与多样性搜索的冲突。仿真实验表明,解的质量提高了,验证该算法有效。

强化Dynasearch & TS算法求解酸轧生产调度问题

酸轧生产调度的主要任务是在满足酸轧机组生产工艺和能力约束下,考虑下游机组的流向需求,为保证生产连续性和平滑过渡的要求,从给定候选池中选择适合的板卷构成一个酸轧调度单元.针对此问题,本文建立了以最小化过渡费用和调度单元剩余容量惩罚费用为目标的整数规划模型,提出了一种嵌入强化Dynasearch算法的禁忌搜索混合算法.该混合算法采用基于最小插入法的两阶段启发式产生初始解,根据采用邻域结构的不同设计双禁忌表,为了避免算法陷入局部最优,在禁忌搜索的每次迭代过程中嵌入Swap邻域和Inner-insert邻域相结合的多交换Dynasearch邻域,并设计了多项式动态规划算法搜索该邻域.针对问题的特征,提出了Block分区结构,基于此分析了多个可行解性质,有效降低了搜索空间.与一般禁忌搜索算法比较,结果表明所提出的强化Dynsearch & TS(Tabusearch)算法求解效果明显优于一般TS算法,平均改进量为3.62%,算法运行时间大大缩短.验证了该算法在解决此类问题的有效性.

一种带时间窗和容量约束的车辆路线问题及其TabuSearch算法

本文提出一种带时间窗和容量约束的车辆路线问题 (CVRPTW ) ,并利用TabuSearch快速启式算法 ,针对Solomon提出的几个标准问题 ,快捷地得到了优良的数值结果。

Tabu Search中集中性和多样性的自适应搜索策略

近年来的研究表明 ,集中性与多样性策略在禁忌搜索中是非常重要的 但集中性与多样性常常又是矛盾的 ,如何解决集中性与多样性之间的矛盾就成为一个值得关注的话题 以组合优化中的著名难题TSP(travelingsalesmanprob lem)为例 ,提出了一种新颖的自适应搜索策略 ,通过邻域和候选集的相互配合 ,动态地调整候选集中分别用于集中性搜索与多样性搜索的元素个数 ,较好地解决了集中性与多样性的冲突问题 仿真实验表明 。

VRPTW的扰动恢复及其TABUSEARCH算法

本文对带时间窗的车辆路线安排扰动恢复问题进行了讨论,分析了各种可能的扰动:增加减少客户,时间窗、客户需求及路线可行性的扰动,构造了扰动模型.利用禁忌搜索算法对问题进行求解,同时通过对模型参数重新设置,得到了多个满足要求的不同的解,这样使解更具有实际可行性和有效性.

一种改进的Tabu Search算法及其在区域电网无功优化中的应用

提出将改进的Tabu(禁忌)搜索算法用于区域电网无功电压优化控制问题的求解.首先根据已知的实际电网的历史数据获得可行的初始解,然后对区域电网采用改进的禁忌搜索方法进行无功优化.在求解的过程中,由于对Tabu表中所记录的"移动"采取"有条件地释放Tabu表中的记录"这一策略,可以使搜索有效地跳出局部极小值点,更好地找到最优解.通过IEEE-14节点算例验证了该算法的有效性.

交通信号实时配时模型及Tabu Search算法

针对单点信号交叉口 ,提出了一种新的信号实时配时模型 .该模型能更好地反映各种交通状况的实际需要 ,其加权系数 ,能随交通需求的变化而实时变化 .并用禁忌搜索算法 ,求出其近似解 .

用Tabu Search解决基于结群的电路划分问题

电路划分是VLSI物理设计中最重要的步骤之一。本文提出了一种自底向上的结群策略,首先将具有高互连关系的电路模块进行结群,然后再将结群后的宏模块进行划分,用Tabu Search启发式算法进行求解,测试电路选择标准MCNC benchmarks,实验结果表明在解的质量相当情况下,运算时间较少。

Tabu Search算法在优化配送路线问题中的应用

将ＴＳ算法应用到物流系统的配送路线优化问题中。在给出了此类问题的描述后，着重阐述了ＴＳ启发式算法的设计，编程实现此算法的要点。最后，用模拟算例对设计的算法进行了验证，计算结果是比较理想的。

基于Tabu搜索的彩色图像可逆数据隐藏算法

针对当前算法对彩色图像进行可逆隐藏时无法获取最优嵌入数据,存在综合性能差以及加密性能差的问题,提出基于Tabu搜索的彩色图像可逆数据隐藏算法,首先对彩色图像进行加密,确定嵌入的位置,利用Tabu搜索算法提取出最优嵌入秘密数据,在加密图像时进行秘密数据的嵌入,对秘密信息以及图像进行预处理;其次接收者利用自带的密钥对已经嵌入数据的加密图像进行解密,同时对图像进行翻转恢复以及信息提取,实现彩色图像可逆数据隐藏。实验结果表明,所提算法的综合性能高以及加密性较好。

Tabu search for no-wait flowshop scheduling problem to minimize maximum lateness

In order to solve the no-wait flowshop scheduling problem to minimize the maximum lateness,three job-block-based neighborhoods are proposed,among which the block exchange neighborhood have a size of O（n4）while the block swap and the simplified block exchange neighborhoods have a size of O（n3）.With larger sizes than the existing neighborhoods,the proposed neighborhoods can enhance the solution quality of local search algorithms.Speedup properties for the neighborhoods are developed,which can evaluate a neighbor in constant time and explore the neighborhoods in time proportional to their proposed sizes. Unlike the dominance-rule-based speedup method,the proposed speedups are applicable to any machine number.Three neighborhoods and the union of block swap and the simplified block exchange neighborhoods are compared in the tabu search.Computational results on benchmark instances show that three tabu search algorithms with O（n3）neighborhoods outperform the existing algorithms and the tabu search algorithm with the union has the best performance among all the tested algorithms.

A hybrid algorithm based on tabu search and large neighbourhood search for car sequencing problem

The car sequencing problem(CSP)concerns a production sequence of different types of cars in the mixed-model assembly line.A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations.Firstly,the hybrid algorithm is based on the tabu search and large neighborhood search(TLNS),servicing as the framework.Moreover,two components are incorporated into the hybrid algorithm.One is the parallel constructive heuristic(PCH)that is used to construct a set of initial solutions and find some high quality solutions,and the other is the small neighborhood search(SNS)which is designed to improve the new constructed solutions.The computational results show that the proposed hybrid algorithm(PCH+TLNS+SNS)obtains100best known values out of109public instances,among these89instances get their best known values with100%success rate.By comparing with the well-known related algorithms,computational results demonstrate the effectiveness,efficiency and robustness of the proposed algorithm.

基于Tabu搜索算法真实世界中喜炎平注射液治疗儿童肺炎的联合用药复杂网络研究

目的挖掘喜炎平注射液治疗儿童肺炎核心联用药物方案的临床应用规律,为探索临床不同诊疗思路、用药经验和提高中医药临床证据的循证等级提供参考。方法本研究基于全国29家医院信息管理系统(Hospital Information System,HIS)儿童肺炎的用药数据,运用Tabu禁忌搜索算法,对真实世界喜炎平注射液治疗儿童肺炎人群的联合用药情况进行回顾性数据挖掘分析。结果在核心联用西药方面,抗感染治疗可以联用青霉素/美洛西林/阿莫西林、头孢呋辛/头孢曲松/头孢替安、阿奇霉素等;对症治疗可以联用对乙酰氨基酚/布洛芬、氨溴索+布地奈德+沙丁胺醇等;并发症治疗可以联用水合氯醛+苯巴比妥、磷酸肌酸+抗坏血酸等。在核心联用中药方面,可以联用小柴胡颗粒/小儿柴桂退热颗粒+鼻渊通窍颗粒、热毒宁注射液/蓝芩口服液/连花清瘟胶囊+开喉剑喷雾剂/口腔炎喷雾剂/双料喉风散、小儿肺咳颗粒+醒脾养儿颗粒/四磨汤口服液等。结论本研究的喜炎平注射液核心联用中西药方案,基本符合相关指南及诊疗规范,为优化临床联合用药、合理用药提供了一定的指导和参考。建议临床实际应用过程中,根据患儿的疾病进展情况,合理评估临床联合用药方案的疗效及安全性,注意用药配伍禁忌。

Channel Assignment Method Using Parallel Tabu Search Based on Graph Theory in Wireless Sensor Networks

Wireless sensor networks are suffering from serious frequency interference.In this paper,we propose a channel assignment algorithm based on graph theory in wireless sensor networks.We first model the conflict infection graph for channel assignment with the goal of global optimization minimizing the total interferences in wireless sensor networks.The channel assignment problem is equivalent to the generalized graph-coloring problem which is a NP-complete problem.We further present a meta-heuristic Wireless Sensor Network Parallel Tabu Search(WSN-PTS) algorithm,which can optimize global networks with small numbers of iterations.The results from a simulation experiment reveal that the novel algorithm can effectively solve the channel assignment problem.

Enhancing the synchronizability of networks by rewiring based on tabu search and a local greedy algorithm

By considering the eigenratio of the Laplacian matrix as the synchronizability measure, this paper presents an efficient method to enhance the synchronizability of undirected and unweighted networks via rewiring. The rewiring method combines the use of tabu search and a local greedy algorithm so that an effective search of solutions can be achieved. As demonstrated in the simulation results, the performance of the proposed approach outperforms the existing methods for a large variety of initial networks, both in terms of speed and quality of solutions.

Optimal Polygonal Approximation of Digital Planar Curves Using Genetic Algorithm and Tabu Search

Three heuristic algorithms for optimal polygonal approximation of digital planar curves is presented. With Genetic Algorithm (GA), improved Genetic Algorithm (IGA) based on Pareto optimal solution and Tabu Search (TS), a near optimal polygonal approximation was obtained. Compared to the famous Teh chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error. Compared to the dynamic programming algorithm, the processing time of our algorithms are much less expensive.