动态自适应蚁群算法求解TSP问题

针对基本蚁群算法容易出现早熟和停滞现象的缺点,提出一种动态自适应蚁群算法,通过引入信息素的自适应调整策略,限制信息素范围以及动态增加信息素的局部更新方式,有效抑制收敛过程中的停滞现象,提高算法的搜索能力．该算法的性能在中国旅行商问题（China Traveling Salesman Problem,CTSP）和EilSO问题上得到验证．     

求解TSP问题的自适应邻域遗传算法

提出结合自适应邻域法与遗传算法来求解TSP问题。在自适应邻域法中,从某个城市出发,下一城市不一定是其最近城市,而是在比其最近城市稍远的邻域范围进行动态随机选取。在求解TSP时,采用自适应邻域法对种群初始化,然后采用选择、交叉、变异进行迭代,在选择中仅保留父代90％的样本,剩下的采用自适应邻域法产生新样本进行补充。仿真实验结果表明所提算法与其他算法相比具有竞争能力。     

通过多种群协进化Memetic算法求解TSP

针对对称TSP提出了多种群协进化Memetic算法（MCMA）．该算法以Memetic算法为基础,采用3个子种群协同进化的方式,克服了Memetic算法由于缺乏种群多样性而产生早熟收敛的缺陷．MCMA中对3个子种群分别引入了2-exchange、3-exchange和PCV三种不同的邻域搜索结构,非常有效地保持了种群的多样性,并且能快速收敛．文中通过对若干TSPLIB中TSP实例的实验仿真来说明所提算法的性能,并且与SGA、SMA和GGA算法进行了比较．通过仿真实验,该算法能够给出相当满意的结果,从而说明了该算法的有效性．     

智能优化算法求解TSP问题

TSP（旅行商）问题代表组合优化问题,具有很强的工程背景和实际应用价值,但至今尚未找到非常有效的求解方法．为此,讨论了最近研究比较热门的使用各种智能优化算法（蚁群算法、遗传算法、模拟退火算法、禁忌搜索算法、Hopfield神经网络、粒子群优化算法、免疫算法等）求解TSP问题的研究进展,指出了各种方法的优缺点和改进策略．最后总结并提出了智能优化算法求解TSP问题的未来研究方向和建议．     

求解TSP问题的改进模拟退火算法

通过分析传统模拟退火算法的原理和存在的不足,提出了一个用于求解TSP问题的改进模拟退火算法。新算法增加了记忆当前最好状态的功能以避免遗失当前最优解,并设置双阈值使得在尽量保持最优性的前提下减少计算量。根据TSP和SA的特征设计了个体邻域搜索方法和高效的计算能量增量方法,加快了算法的运行速度。实验测试的结果表明,新算法比传统的模拟退火算法具有更快的收敛速度和更优的解质量。     

求解TSP问题的自适应离散型布谷鸟算法

对于求解的TSP问题,提出了一种自适应离散型布谷鸟算法（Adaptive Discrete Cuckoo Search,ADCS）。在基于布谷鸟搜索算法（Cuckoo Search,CS）的搜索原理下构造TSP问题的路径求解策略。针对离散型算法整体调整容易破坏已形成的较优路径和随着算法迭代数目增加导致种群多样性下降这两个缺陷,设计了一种针对路径的自适应型局部调整算子和全局随机扰动策略,采用了简单的2-opt优化算子作为局部优化算子以加快算法的收敛速度。最后采用多组不同规模的标准TSPLIB数据与其他的优化算法进行对比实验,结果表明ADCS算法在求解精度和稳定性方面具有优势。     

动态蚁群算法求解TSP问题

蚂蚁群体能完成单个蚂蚁所无法完成的工作。它们通过称为信息素的物质交流信息而协同工作。蚂蚁在觅食活动中,在食物与巢穴之间的路径上留下信息素,较短路径信息素相对较浓,而蚂蚁倾向于沿信息素较浓的路径往返于巢穴与食物之间。经过一段时间后,就可发现从巢穴到食物的较短的路径。基于此原理,MarcoDorigo提出了蚁群算法,并首先用于求解TSP问题。该文从更多方面模仿真实自然界中蚂蚁的行为,更为合理地制定信息素动态挥发规则,提出动态蚁群算法并用于解决TSP问题,实验表明了该算法有较好的性能。     

求解TSP问题算法综述

TSP问题（旅行商问题）是一个典型的组合优化问题,具有重要实际应用价值。对于大规模TSP问题,至今尚未找到非常有效的求解方法。为此,本文讨论了传统的确定性算法和流行的智能算法,并指出各种方法的优缺点,提出了未来求解TSP问题的发展趋势。     

一种适用于求解TSP问题的改进的禁忌算法

利用传统的禁忌算法的基本思想,针对TSP问题,提出了一种改进的禁忌算法（MTS）。该算法在初始解的生成,邻域结构及禁忌策略方面进行了大的改进,充分地利用了问题本身的启发式信息与禁忌算法的优点。算法首先通过对城市分区,然后对区域连接,生成初始解;同时生成每个城市的k邻居列表,利用k邻居列表和改进的禁忌策略来突破局部最优。通过对CHN144问题及若干TSPLIB中问题的求解,结果表明所提算法能够以较快速度求得较好的满意解。     

自适应蚁群算法在求解TSP问题中的应用

围绕蚁群优化算法的理论及应用,针对蚁群算法在TSP规划中求解能力不足的难题,运用了一种基于自适应的蚂蚁算法,并对TSP规划进行了设计。为了提高路径规划的效率,将自适应与传统的蚂蚁算法相结合形成了自适应蚁群算法。仿真实验结果表明,改进后算法能够在较短时间内找到全局最优路径,相对于基本的蚁群算法在收敛速度、搜索质量和局部寻优方面都有了明显的提高。     

基因-表现型的布谷鸟算法求解旅行商问题

布谷鸟搜索（Cuckoo Search,CS）算法在求解连续优化问题时表现出了较好的性能,但现有的CS算法在求解旅行商问题（Traveling Salesman Problem,TSP）时收敛较慢且未能体现Levy飞行的特点,针对这些不足提出了一种新的基因-表现型的布谷鸟算法（Genotype-Phenotype Cuckoo Search,GPCS）,GPCS算法首先赋予每个城市一个整数部分为城市编号的随机小数编码即基因,而此基因所表现的内容由小数和整数共同决定,小数决定城市的访问次序,整数部分代表某个城市,两个部分组合起来构成Levy飞行的邻域空间,最后根据不同的飞行结果选择重定位或替换操作。实验结果表明,GPCS算法优于同类的CS算法,也优于一些其他的群智能算法,特别在求解大规模TSP时其优势更加明显。     

求解非连通图旅行商问题的改进遗传算法

为了克服传统遗传算法的早熟收敛问题,提出改进遗传算法。采用基于旅行商遍历城市顺序的染色体编码,结合随机法与贪心法生成初始种群,提高遗传效率。通过执行优先保留交叉和平移变异操作,引入局部邻域搜索,给出最优解是否满足非连通约束的判据。最后,实验结果验证了该算法的有效性。     

指针网络改进遗传算法求解旅行商问题

虽然遗传算法相较于其他算法能够更好地求解旅行商问题,但这种算法在使用的过程中容易陷入局部最优的问题,进而导致问题求解遭遇困境。文章在简要介绍旅行商问题的基础上,介绍了遗传算法求解旅行商问题的思路和方法,并明确算法应用中存在的不足。在此基础上提出基于指针网络改进遗传算法求解旅行商问题的新思路,为弥补遗传算法的缺陷提供相应的原理支持。     

A variable neighborhood search for flying sidekick traveling salesman problem

The efficiency and dynamism of unmanned aerial vehicles, or drones, have presented substantial application opportunities in several industries in the last years. Notably, logistic companies have given close attention to these vehicles to reduce delivery time and operational cost. A variant of the traveling salesman problem (TSP), called the flying sidekick traveling salesman problem, was introduced involving drone‐assisted parcel delivery. The drone launches from the truck, proceeds to deliver parcels to a customer, and then is recovered by the truck at a third location. While the drone travels through a trip, the truck delivers parcels to other customers as long as the drone has enough battery to hover waiting for the truck. This work proposes a hybrid heuristic where the initial solution is created from the optimal TSP solution reached by a TSP solver. Next, an implementation of the general variable neighborhood search is employed to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve significantly delivery time. Furthermore, we provide a new set of instances based on the well‐known traveling salesman problem library instances.     

Two level General variable neighborhood search for Attractive traveling salesman problem

Attractive traveling salesman problem (AtTSP) consists of finding maximal profit tour starting and ending at a given depot after visiting some of the facilities. Total length of the tour must not exceed the given maximum distance. Each facility achieves profit from the customers, based on the distance between the facility and customers as well as on the attractiveness of that facility. Total profit of a tour is equal to a sum of profits of all visited facilities. In this paper, we develop a new variant of Variable neighborhood search, called 2-level General variable neighborhood search (2-GVNS) for solving AtTSP. At the second level, we use General variable neighborhood search in the local search lor building neighboring solution and checking its feasibility. Our 2-GVNS heuristic outperforms tabu search heuristic, the only one proposed in the literature so far, in terms of precision and running times. In addition, 2-GVNS finds all optimal known solutions obtained by Branch and cut algorithm and offers several new best known solutions.     

求解最小比率旅行商问题的离散蝙蝠算法

研究了以总路程与总收益之比为目标函数的最小比率旅行商问题,提出了求解该问题的离散蝙蝠算法。介绍了蝙蝠算法的基本思想,重新定义了位置与位置的减法操作算子、实数与位置的乘法操作算子以及速度与位置的加法操作算子,引入了城市子序列逆序策略来对线路进行局部搜索。给出了算法的具体实现方案,并通过仿真和比较实验验证算法的优化性能,实验结果表明该算法可以有效求解最小比率旅行商问题。     

The double traveling salesman problem with multiple stacks: A variable neighborhood search approach

The double traveling salesman problem with multiple stacks (DTSPMS) is a vehicle routing problem that consists on finding the minimum total length tours in two separated networks, one for pickups and one for deliveries. A set of orders is given, each one consisting of a pickup location and a delivery location, and it is required to send an item from the former location to the latter one. Repacking is not allowed, but collected items can be packed in several rows in such a way that each row must obey the LIFO principle. In this paper, a variable neighborhood search approach using four new neighborhood structures is presented to solve the problem.     

On-orbit servicing: a time-dependent, moving-target traveling salesman problem

The robotic capability of maintaining and repairing space assets, on‐orbit servicing (OOS), has the potential to change the way spacecraft are designed, manufactured and operated. The most common OOS mission concept envisions an orbital “depot”, where consumables and spare parts for spacecraft will be stored. A “servicing platform”, based at this depot, will be used to service a number of client spacecraft and then return to the depot for resupply. We model OOS as a time‐dependent, moving‐target traveling salesman problem and present an algorithm for minimizing the total amount of energy or time required for OOS operations.     

A hybrid scatter search for the probabilistic traveling salesman problem

The probabilistic traveling salesman problem (PTSP) is an important theoretical and practical topic in the study of stochastic network problems. It provides researchers with a modeling framework for exploring the stochastic effects in routing problems. This paper focuses on developing the hybrid scatter search (HSS) by incorporating the nearest neighbor rule (NNR), threshold accepting (TA) and edge recombination (ER) crossover into a scatter search conceptual framework to solve the PTSP. A set of numerical experiments were conducted to test the validity of the HSS based on the test problems from Tang and Miller-Hooks’ study. The numerical results showed that the HSS can effectively solve the PTSP in most of the tested cases in terms of objective function value. Moreover, the results also indicated that incorporating threshold accepting into the scatter search framework can further increase the computation efficiency while maintaining solution quality. These findings show the potential of the proposed HSS in solving the large-scale PTSP.     

基于文化混合优化算法的旅行商问题求解

为更好地求解TSP问题,将遗传算法与模拟退火算法结合并纳入文化算法体系,提出一种求解旅行商问题的文化混合优化算法。该算法空间可分为独立并行的两部分：种群空间和信度空间。种群空间按照遗传退火混合算法实现进化,并将进化中的较优个体提供给信度空间,信度空间提取并利用较优个体所包含的信息来引导种群进化。通过求解TSP标准测试问题,将文化混合优化算法所求得的最优路径与其他优化算法所求结果相比,算法偏差均可降低0.6%～13.01%,表明了文化混合优化算法求解TSP问题的有效性与优越性。