粒子群优化算法车辆路径规划软件实现+源程序

摘要21 世纪以来，随着电子信息的发展，物流业迅速发展起来。由于运输是物流系统中最为重要的一部分，路径规划是运输的核心问题，因此对路径规划问题的研究也越显重要。据不完全统计，运输费用约占总物流费用的 50%之多，因此，如何降低运输成本成为提升企业收益的关键问题。路径规划问题能够有效的解决车辆运输路线的选择问题，节约运输成本的同时还能有效的缓解交通压力。本文针对这类问题，采用粒子群优化算法(PSO)进行研究，它是一种智能化的全局随机搜索法，能够有效的解决路径规划问题。84866

本文从最短路径问题出发，介绍了车辆路径规划问题的一般描述和常用解决方法。详细介绍了粒子群算法的基本概念、原理及应用。将粒子群算法应用于关于最短路径的实际问题中，对于地图中给定的两个城市(或两个地点)，利用这种算法找到这两座城市(或两个地点)的最短路线，并求出其长度。在认真分析国内外对 PSO 算法的研究基础上，对算法进行合理优化，选择采用惯性权重自适应的优化方法，来提高算法的效率和准确性，利用 MATLAB 编程实现算法的运行，对求解结果进行可视化。

毕业论文关键词：路径规划问题；最短路径问题；粒子群算法(PSO)；算法优化；惯性权重自适应

AbstractSince the 21st Century, with the development of electronic information，the logistics industry has developed rapidly。 Since the transport logistics system is the most important part, and path planning is the core issue of transport,therefore, the study of path planning is increasingly important。 According to incomplete statistics, transport costs accounted for about 50% of the total logistics costs。 Therefore, how to reduce the transportation cost has become the key problem to improve the enterprise income。 Path planning problem can effectively solve the vehicle routing problem,it not only can save the transportation cost but also can effectively alleviate the traffic pressure。 This article is aimed at this kind of problem, the particle swarm optimization (PSO) is used to study the problem。 It is a kind of intelligent global random search method, which can effectively solve the problem of path planning。

Starting from the shortest path problem, this paper introduces the general description of the vehicle routing problem and the common solutions。 The basic concept, principle and application of particle swarm optimization algorithm are introduced in detail。 The particle swarm optimization algorithm is applied to the practical problem of the shortest path。 For a given two cities (or two locations) in the map,using this algorithm to find the shortest route of the two cities (or two locations),and finding its length。 On the basis of careful analysis

of the domestic and foreign research on the PSO algorithm, the algorithm is optimized。 In order to improve the efficiency and accuracy of the algorithm, the inertia weight adaptive optimization method is chosen。 Using MATLAB programming algorithm to achieve the operation and making the solution result can be considered。

Keywords: Path planning problem;Shortest path problem;Particle swarm optimization (PSO);Algorithm optimization;Adaptive inertia weight

第一章绪论 1

1。1 研究背景及选题意义 1

1。2 国内外研究现状粒子群优化算法车辆路径规划软件实现+源程序:http://www.youerw.com/zidonghua/lunwen_101058.html