求解大规模旅行商问题的改进大洪水算法

大洪水算法是通过模拟洪水上涨过程来进行全局寻优的启发式算法,r-opt算法是一类常用的路径改进算法.本文针对旅行商问题,提出一种将二者有机融合的改进大洪水算法,可用于快速求解大规模和超大规模的TSP问题.算法在Delphi7环境下编程实现,经过大量TSPLIB中的数据实例进行测试和验证,求解结果与已公布的最好结果误差基本都在1％以下,为困难的大规模旅行商问题提供了新的求解手段.     

瓶颈TSP的蚂蚁系统优化

对瓶颈TSP问题给出了一种融合局部搜索机制和MAX－MIN策略的蚂蚁优化算法，在通用微机上求解了一系列实例问题，获得了满意的效果。     

利用确定性退火技术的旅行商问题求解算法*

将确定性退火技术及聚类方法应用于旅行商问题,给出了求解旅行商问题的一种启发式算法.该方法将旅行商问题的离散模型转化为连续模型去求解,通过求解一系列随温度变化的物理系统的自由能函数的局部极小来获得旅行商问题的解,并给出了一个简单的显式迭代公式.算例表明,该算法性能良好.     

自适应蚁群算法在流水车间调度的应用

以求解旅行商问题(TSP)来介绍基本蚁群算法模型.针对其存在的易陷入局部最优和易出现停滞等缺点,将自适应调节策略与蚁群算法结合,提出应用改进的蚁群算法求解流水车间调度问题,并通过仿真实验验证了该改进算法的有效性和优化性.     

Solving fuzzy assembly-line balancing problem with genetic algorithms

Assembly-line balancing problem is known as one of difficult combinatorial optimization problems. This problem has been solved with linear programming, dynamic programming approaches, but unfortunately these approaches do not lead to efficient algorithms. Recently, genetic algorithm has been recognized as an efficient and usefull procedure for solving large and hard combinatorial optimization problems, such as scheduling problems, travelling salesman problems, transportation problems, and so on. Fuzzy sets theory is frequently used to represent uncertainty of information. In this paper, to treat the data of real-world problems we use a fuzzy number to represent the processing time and show that we can get a good performance in solving this problem using genetic algorithms.     

基于遗传算法的多人旅行商问题求解

旅行商问题是一个经典的XP完全问题，多人旅行商问题的求解则更具挑战性。以往对求解多人旅行商问题的研究局限于以所有成员路径总和最小为优化标准，面对以所有成员路径最大值最小为优化标准的另一类多人旅行商问题却未加注意。文章给出了这两类多人旅行商问题的形式化描述，探讨了利用遗传算法求解这两类多人旅行商问题的基本思想和具体方案，进行了仿真实验验证。仿真实验数据表明，这是一种高效而且适应性强的多人旅行商问题求解方法。     

大规模旅行商问题的竞争决策算法

针对旅行商问题,利用竞争决策算法的通用模型,给出了一种基于竞争决策思想,能求解大规模和超大规模TSP问题的快速求解方法,经过大量数据测试和验证,获得了较好的结果.     

基于蚁群算法的中国旅行商问题满意解

蚁群算法是基于群体合作的一类仿生算法，适合于解困难的离散组合优化问题。本文对其做了适当的改进，以克服其求解速度过慢、容易出现停滞的缺陷，并将其用于解决中国旅行商问题。找到了目前巳知的最好的解，同时指出了进一步提高蚁群算法效率还需解决的问题和方向。     

The travelling salesman problem with time windows: Adapting algorithms from travel-time to makespan optimization

In combinatorial optimization it is not rare to find problems whose mathematical structure is nearly the same, differing only in some aspect related to the motivating application. For example, many problems in machine scheduling and vehicle routing have equivalent formulations and only differ with respect to the optimization objective, or particular constraints. Moreover, while some problems receive a lot of attention from the research community, their close relatives receive hardly any attention at all. Given two closely related problems, it is intuitive that it may be effective to adapt state-of-the-art algorithms—initially introduced for the well-studied problem variant—to the less-studied problem variant. In this paper we provide an example based on the travelling salesman problem with time windows that supports this intuition. In this context, the well-studied problem variant minimizes the travel time, while the less-studied problem variant minimizes the makespan. Indeed, the results show that the algorithms that we adapt from travel-time minimization to makespan minimization significantly outperform the existing state-of-the-art approaches for makespan minimization.     

基于深度强化学习种群优化的演化式分拣调度算法

机械制造中的产线分拣作业具有问题与数据的双重复杂性,为了对分拣操作进行优化以提高生产效率,设计了一套分拣作业的数据表示方法与一种基于种群优化的演化式算法,同时整理并公开了一个真实的工业数据集。数据表示方法通过借鉴词袋模型对原始作业数据进行抽象表示;演化式算法使用深度强化学习初始化遗传算法中的种群,同时引入了精英保留策略以提高算法的优化能力。最后,将提出的算法与其他算法在真实的工业数据集与旅行商问题数据集上进行了对比。结果表明,该算法能找到更优的分拣顺序与访问路径,验证了算法的有效性。     

带固定半径近邻搜索3-opt的离散烟花算法求解旅行商问题

传统烟花算法求解大规模离散问题存在收敛速度慢、求解精度不高等问题.针对旅行商问题的特点,提出一种带固定半径近邻搜索3-opt的离散烟花算法.该算法基于基本烟花算法进行离散化改进,采用整数编码的路径表示方法来表示旅行商问题的解,对爆炸算子、高斯变异算子进行离散化操作策略设计.为了使算法具有较好的局部搜索能力,提出固定半径近邻搜索3-opt策略来提高算法精度和收敛速度,同时采用不检测标志策略提高算法效率.实验结果表明:该算法能有效地求解旅行商问题,其离散烟花算子在全局收敛能力、收敛精度、求解时间和稳定性等方面均优于传统烟花算子;基准测试算例的最优解平均误差率仅为0.002％,优于对比算法.     

Darts and hoopla board design

Dartboard design can be seen as an instance of the travelling salesman problem with maximum costs. This paper presents a simple yet optimal greedy algorithm to arrange numbers on both circular dartboards and linear hoopla boards. As a result, it identifies a class of polynomially solvable travelling salesman problems.     

带软时间窗的旅行商问题的双种群遗传算法

论文运用双种群遗传算法求解带软时间窗的旅行商问题,通过加入带有时间窗约束条件的惩罚函数,初始化两个种群,分别选择不同的交叉、变异概率。每次迭代后,交换种群间的优势个体所携带的遗传信息,以打破种群内的平衡状态,跳出局部最优解。双种群遗传算法比标准遗传算法显著提高了全局收敛性能。实验结果比较显示,该算法行之有效,具有较好的性能。     

基于TSP方法求解等待时间受限的置换流水车间调度

等待时间受限的置换流水车间调度问题要求工件在连续两个机器间的等待时间满足上限值约束.对此,分析了工件序列中相邻工件的加工持续时间及其上下界关系,并且提出一种启发式方法.首先,建立旅行商间题（TSP）以生成初始调度;然后,采用扩展插入方法优化调度解.为了衡量算法性能,给出问题下界的计算方法和相关评价指标,并通过数据实验验证了该启发式和下界计算方法的可行性和有效性.     

货郎担问题最优并行启发式算法

本文给出了满足三角不等式的货郎担问题的并行启发式算法，在ＳＩＭＤ ＣＲＥＶ ＰＲＡＭ并行机上该算法使用Ｏ（ｎ＾３／ｌｏｇ＾２ｎ）台处理器需Ｏ熄ｌｏｇ＾２ｎ）时间，这里ｎ是给定城市的个数，因而该并行算法是最优的。     

利用粒子群优化算法求解圆排列问题

针对已有算法搜索时间较长,且易于过早地收敛于非最优解的缺陷,利用粒子群优化算法给出了圆排列问题的求解方法.首先,在分析了圆排列问题与旅行商问题关系的基础上,将圆排列问题转化为旅行商问题,从而得到一个相应的组合优化问题.然后,利用粒子群优化算法进行了求解.接着,为了进一步提高算法的精度,文中给出了一种利用混合粒子群优化算法的方案.最后,在仿真实验中,与已有算法进行了比较,实验结果表明,文中所给方法是有效的.     

PCB数控钻孔最佳走刀路线的建模与求解

目前,采用PCB数控钻自动编程系统生成的钻孔路线并非最佳走刀路线。通过分析,将PCB数控钻孔最佳走刀路线问题归结为大型TSP问题,其目标函数定为钻头的总走刀时间最短。由于TSP问题在理论上属于NP完备问题,因此很难用一般的算法求解。文中详细介绍了用模拟退火方法求解该问题的具体算法,并以上继基础开发了PCB的最优化的自动编程系统。     

A novel discrete bat algorithm for solving the travelling salesman problem

The travelling salesman problem (TSP) is one of the well-known NP-hard combinatorial optimization and extensively studied problems in discrete optimization. The bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. In this paper we extend a discrete bat-inspired algorithm to solve the famous TSP. Although many algorithms have been used to solve TSP, the main objective of this research is to investigate this discrete version to achieve significant improvements, not only compared to traditional algorithms but also to another metaheuristics. Moreover, this study is based on a benchmark dataset of symmetric TSP from TSPLIB library.     

A hybrid swarm intelligence algorithm for the travelling salesman problem

Abstract: We present a hybrid model named HRKPG that combines the random‐key search method and an individual enhancement scheme to thoroughly exploit the global search ability of particle swarm optimization. With a genetic algorithm, we can expand the area of exploration of individuals in the solution space. With the individual enhancement scheme, we can enhance the particle swarm optimization and the genetic algorithm for the travelling salesman problem. The objective of the travelling salesman problem is to find the shortest route that starts from a city, visits every city once, and finally comes back to the start city. With the random‐key search method, we can search the ability of the particle and chromosome. On the basis of the proposed hybrid scheme of HRKPG, we can improve solution quality quite a lot. Our experimental results show that the HRKPG model outperforms the particle swarm optimization and genetic algorithm in solution quality.     

改进蚁群优化方法求解立体表面TSP问题

给出立体表面TSP问题的数学模型,提出一种改进的蚁群优化算法,用于解决立体表面TSP问题。该算法能快速找到最优路径或近似最优路径,得到的解质量较高且计算时间短。实验方法表明,改进后的蚁群算法在TSP的求解中,收敛速度和全局寻优能力均得到较大的提高。