An application of the self-organizing map in the non-Euclidean Traveling Salesman Problem

An application of the self-organizing map (SOM) to the Traveling Salesman Problem (TSP) has been reported by many researchers, however these approaches are mainly focused on the Euclidean TSP variant. We consider the TSP as a problem formulation for the multi-goal path planning problem in which paths among obstacles have to be found. We apply a simple approximation of the shortest path that seems to be suitable for the SOM adaptation procedure. The approximation is based on a geometrical interpretation of SOM, where weights of neurons represent nodes that are placed in the polygonal domain. The approximation is verified in a set of real problems and experimental results show feasibility of the proposed approach for the SOM based solution of the non-Euclidean TSP.     

On the performance of self-organizing maps for the non-Euclidean Traveling Salesman Problem in the polygonal domain

In this paper, two state-of-the-art algorithms for the Traveling Salesman Problem (TSP) are examined in the multi-goal path planning problem motivated by inspection planning in the polygonal domain W. Both algorithms are based on the self-organizing map (SOM) for which an application in W is not typical. The first is Somhom’s algorithm, and the second is the Co-adaptive net. These algorithms are augmented by a simple approximation of the shortest path among obstacles in W. Moreover, the competitive and cooperative rules are modified by recent adaptation rules for the Euclidean TSP, and by proposed enhancements to improve the algorithms’ performance in the non-Euclidean TSP. Based on the modifications, two new variants of the algorithms are proposed that reduce the required computational time of their predecessors by an order of magnitude, therefore making SOM more competitive with combinatorial heuristics. The results show how SOM approaches can be used in the polygonal domain so they can provide additional features over the classical combinatorial approaches based on the complete visibility graph.     

A Sensor Placement Algorithm for a Mobile Robot Inspection Planning

In this paper, we address the inspection planning problem to ??see?? the whole area of the given workspace by a mobile robot. The problem is decoupled into the sensor placement problem and the multi-goal path planning problem to visit found sensing locations. However the decoupled approach provides a feasible solution, its overall quality can be poor, because the sub-problems are solved independently. We propose a new randomized approach that considers the path planning problem during solution process of the sensor placement problem. The proposed algorithm is based on a guiding of the randomization process according to prior knowledge about the environment. The algorithm is compared with two algorithms already used in the inspection planning. Performance of the algorithms is evaluated in several real environments and for a set of visibility ranges. The proposed algorithm provides better solutions in both evaluated criterions: a number of sensing locations and a length of the inspection path.     

Variants of the Quantized Visibility Graph for Efficient Path Planning

We propose variants of the quantized visibility graph (QVG) for efficient path planning. Conventional visibility graphs have been used for path planning when the obstacles are polygonal. The QVG extends its usability to arbitrarily-shaped objects by representing the obstacles as polygons. We propose QVG variants which represent all combinations of three factors, each with two alternatives: (i) quantization level (fixed-level or multiple-level), (ii) object representation method (inner and boundary cells together or boundary cells only), and (iii) methods used to check whether pairs of points are mutually visible (rotational plane sweep algorithm or sign inequality discrimination (SID) algorithm). In the verification of the efficiency of the proposed QVGs, (i) all QVGs produced the same best path, which was shorter than the convectional algorithms, (ii) computational cost to find the shortest path is lower when using QVGs than when using the convectional algorithms and (iii) the QVG that uses multi-level quantization, partial obstacle representation and SID visibility checking provides the shortest best path and has lower computational cost than all other methods.     

基于改进粒子群算法的移动机器人多目标点路径规划

针对移动机器人遍历多个目标点的路径规划问题,提出了一种基于改进粒子群算法和蚁群算法相结合的路径规划新方法。该方法将目标点的选择转化为旅行商问题,并利用蚁群算法进行优化,定义了每两个目标点之间的路径规划目标函数,利用粒子群算法对其进行优化。针对粒子群算法存在的早熟现象,将反向学习策略引入粒子群算法,并对粒子群算法的惯性权重和学习因子进行改进。性能测试结果表明,改进的粒子群算法能有效避免粒子早熟现象,提高粒子群算法的寻优能力及稳定性。仿真实验结果验证了新方法能有效地实现机器人的多目标点无碰撞路径规划。真实环境下的实验结果证明了新方法在机器人多目标点路径规划的实际应用中也具有有效性。     

The period traveling salesman problem: a new heuristic algorithm

A new, simple and effective heuristic algorithm has been developed for the period traveling salesman problem. Computational results obtained from the test problems taken from the literature indicate that the algorithm compares well in terms of accuracy with other existing algorithms, finding a larger number of best solutions. Moreover, its average percentage error and its worst ratio of solution to the best-known solution are smaller than those of the other existing algorithms.Scope and purposeIn the period traveling salesman problem, a traveling salesman must visit each city a fixed number of times over a given m-day planning period. Each city specifies a set of sequences of visit days and the visit days are assigned to the city by selecting one of these sequences. Moreover, for each day of the planning period, a not empty tour must be generated by connecting the salesman home city and the cities that must be visited on that day. The salesman objective is to minimize the total distance traveled over the entire m-day period. For this problem, arising in various situations such as mail delivery or lawn-care services, the paper proposes a simple and effective heuristic algorithm where an improvement procedure is embedded within a tour construction type procedure.     

A roadmap construction algorithm for mobile robot path planning using skeleton maps

Path planning using conventional roadmaps, such as visibility graphs, probability roadmaps and skeleton maps, may have some disadvantages of long length, sharp turns or collisions with obstacles. Specifically, the paths using the conventional skeleton map have unnecessary turns around crossing points, which make longer paths and prevent the robot from moving smoothly. To improve the skeleton map, this paper proposes a new roadmap construction algorithm for path planning of a mobile robot using skeleton maps. The proposed algorithm alleviates the problems of the conventional algorithms by constructing roadmaps which consist of polygons around the crossing points. Simulation results show the efficiency of the proposed algorithm by comparing the results with those obtained using the conventional algorithm.     

A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES/spl I.bar/DECOMPOSE

In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.     

基于莱维飞行粒子群算法的焊接机器人路径规划

焊接机器人在工业上被广泛应用,焊接的任务规划直接关系到制造效率的提高.点焊机器人路径规划在仅考虑路径长度时可以简化为焊接顺序的优化问题,即旅行商问题.考虑到旅行商问题是NP完全问题,且是离散问题,提出一种结合莱维飞行的粒子群算法并对其进行离散化以求解此类路径优化问题.焊接机器人路径规划仿真结果验证了所提出方案的合理性和可行性.     

一种基于可视图的机器人避障路径规划

该文首先提出用Minkowski和的工具将多边形机器人的路径规划问题转化为点机器人的情况,然后基于移动机器人的安全考虑,提出了一种改进的可视图法。该方法用尽可能远离障碍物的路径表示弧,先确定可能的路径点作为节点,然后考虑可能路径,建立结点间的弧,并用Dijkstra算法求出图中的最短路径。最后通过仿真研究表明,用文章提出的方法规划的路径可以达到或接近最优路径。     

Optimized design of patrol path for offshore wind farms based on genetic algorithm and particle swarm optimization with traveling salesman problem

With the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.     

基于微粒群算法的城市公交线网模型研究

微粒群算法是求解组合优化问题的一种新的群体智能进化算法,从城市公交乘客选择出行路径的决策因素出发,以微粒群算法进化机理为核心,结合微粒群进化算法中的局部搜索与全局搜索同时进行的优点和运筹学旅行商组合优化理论,系统地建立了规划城市智能交通公交线网最短路径的数学模型进化算法,并通过MATLAB 7.0进行了实例仿真,得到了城市公交线网出行选择模型中总运输里程权重最短的优化目标。仿真结果也表明,该进化算法模型是解决城市公交线网规划的有效方法。     

Solving the traveling salesman problem with annealing-basedheuristics: a computational study

Recently, several general optimization algorithms based on the demon algorithm from statistical physics have been developed and tested on a few traveling salesman problems with encouraging results. In this paper, we conduct an extensive computational study of 11 annealing-based heuristics for the traveling salesman problem. We code versions of simulated annealing, threshold accepting, record-to-record travel and eight heuristics based on the demon algorithm. We apply each heuristic to 29 traveling salesman problems taken from a well-known online library, compare the results with respect to accuracy and running time and provide insights and suggestions for future work     

基于栅格空间的移动机器人快速路径规划方法

基于栅格空间的节点扩展方式,提出了一种移动机器人快速路径规划的新方法;在引人可视性概念的基础上,通过对传统八邻域法扩展节点进行改进,成功将节点间派生关系由物理相邻引申到逻辑相邻,从而完成任意方向的节点扩展;采用启发式搜索算法D*Lite进行搜索,将应用两种扩展方式的算法在VC环境下分别实现并进行了分析比较;仿真结果表明,新算法不仅极大缩短了路径长度而且显著降低了执行时间。文章提出的算法很好地解决了移动机器人快速路径规划问题。     

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。     

双获胜节点SOM及其在TSP中的应用

在Kohonen提出的SOM(self-organization map)神经网络的基础上,通过拓广SOM网络的获胜节点数量,引入惩罚修正因子,改进邻域和连接权函数等方法提出一种新的SOM即SOMDW(SOM with double-winner)模型.为了验证该模型的有效性,以旅行商问题(traveling salesman problem,TSP)为例对该模型进行检验,得到了满意的结果.另外为了增强SOMDW网络的动态聚类性能,提高解的精确性,还采用禁忌搜索的搜索方法.     

Variable neighborhood search algorithm for heterogeneous traveling repairmen problem with time windows

The focus of this paper is generalized traveling repairman problem (TRP), a special case of the well known and well studied traveling salesman problem (TSP). Because of its specific objective function, that minimizes the sum of overall time all clients wait for until the end of a service, TRP has great applicability potential in client oriented practical problems. Therefore it has been known in literature as traveling deliveryman problem, minimum latency problem and cumulative capacitated vehicle routing problem. However, most studies that have treated TRP related problems have implied that only one repairman is present in the system and/or that all clients are available for service at the beginning of the planning horizon. In this paper we consider a TRP with a heterogeneous fleet of repairmen serving a set of clients whose arrival times are distributed over a planning horizon, i.e. heterogeneous TRPTW (hetTRPTW). For the hetTRPTW we present a mixed integer linear programming model, and a heuristic algorithm based on a variable neighborhood search (VNS) framework. Additionally, we propose a reduction strategy for neighborhoods in the VNS algorithm and test efficiency of implemented algorithms on four benchmark sets of problem instances. Results show that proposed algorithms could be used in real systems for solving small and moderate problem instances.     

基于自适应继承策略的帝国竞争算法求解旅行商问题

针对帝国竞争算法在求解旅行商问题时局部搜索能力不强和容易陷入局部最优的缺陷,提出一种基于自适应继承策略的帝国竞争算法.该算法采用自适应继承策略的启发式交叉算子、单点局部插入策略和固定邻域的2-opt算子来增强算法的局部优化能力,并加入帝国精英解集以保持种群的多样性.通过标准实例测试,验证了所提出的改进策略的优越性,与基于启发式交叉算子和帝国主义算法为框架的其他算法进行对比,实验结果表明,该算法求解中小规模的解旅行商问题具有较高的求解精度和较快的收敛速度.     

基于离散灰狼算法的喷涂机器人路径规划方法

针对目前用于复杂结构实体喷涂的机器人路径规划方法存在的效率低、未考虑碰撞以及适用性差等问题,提出一种用于求解多层决策问题的离散灰狼算法,并把该算法用于该路径规划问题的求解。为了将连续域灰狼算法改为用于求解多层决策问题的离散灰狼算法,采用矩阵编码方法解决多层决策问题的编码问题,提出基于先验知识与随机选择的混合初始化方法提高算法求解效率和精度,运用交叉算子与两级变异算子定义离散域灰狼算法的种群更新策略。另外,运用图论将喷涂机器人路径规划问题简化为广义旅行商问题,并建立了该问题的最短路径模型和路径碰撞模型。在路径规划实验中,相较于粒子群算法、遗传算法和蚁群算法,提出的算法规划的平均路径长度分别减小了5.0%、5.5%和6.6%,碰撞次数降低为0,且路径更平滑。实验结果表明,提出的算法能够有效提高喷涂机器人的喷涂效率,以及喷涂路径的安全性和适用性。     

基于离散灰狼算法的喷涂机器人路径规划方法

针对目前用于复杂结构实体喷涂的机器人路径规划方法存在的效率低、未考虑碰撞以及适用性差等问题，提出一种用于求解多层决策问题的离散灰狼算法，并把该算法用于该路径规划问题的求解。为了将连续域灰狼算法改为用于求解多层决策问题的离散灰狼算法，采用矩阵编码方法解决多层决策问题的编码问题，提出基于先验知识与随机选择的混合初始化方法提高算法求解效率和精度，运用交叉算子与两级变异算子定义离散域灰狼算法的种群更新策略。另外，运用图论将喷涂机器人路径规划问题简化为广义旅行商问题，并建立了该问题的最短路径模型和路径碰撞模型。在路径规划实验中，相较于粒子群算法、遗传算法和蚁群算法，提出的算法规划的平均路径长度分别减小了5.0%、5.5%和6.6%，碰撞次数降低为0，且路径更平滑。实验结果表明，提出的算法能够有效提高喷涂机器人的喷涂效率，以及喷涂路径的安全性和适用性。