SWARM INTELLIGENCE: APPLICATION OF THE ANT COLONY OPTIMIZATION ALGORITHM TO LOGISTICS‐ORIENTED VEHICLE ROUTING PROBLEMS

This research evaluates a set of logistics‐oriented vehicle routing problems (VRP) taken from the logistics and supply chain literature under the widely used Clark‐Wright Savings algorithm and the newer metaheuristic method employing a type of swarm intelligence called Ant Colony Optimization (ACO). ACO simulates the decision‐making processes of colonies of ants as they forage for food and is related to other artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Experimentation shows that ACO is successful in finding solutions near the best‐known solutions for problems with up to 20 demand locations. In addition, testing for the affect of spatial patterns suggested by the logistics literature for facility locations appears to make a difference in the quality of the solutions for the two algorithms. Finally, ACO is shown to be superior to the savings algorithm found in software packages and as a result should be tested on even larger, more complex logistics‐oriented vehicle routing problems, representative of those encountered in larger industrial and retail settings.     

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

陕西装备制造业集群竞争力研究

本文选取区位熵法对陕西装备制造业集群集中度进行测量,揭示了陕西装备制造业七个子行业的集群现状,结合产业集群理论和陕西省装备制造业集群特点,选取装备制造业七个子行业的十个指标,运用因子分析对陕西省装备制造业集群竞争力进行评价。结果表明:除专用设备制造业和交通运输制造业集中度比较高之外,其总体产业集群优势较低,且内部结构不合理;作为装备制造业的核心竞争力,科技创新能力是影响装备制造业综合竞争力的最关键因素。在此基础上为陕西装备制造业集群竞争力的提升提出可行的对策。     

基于蚁群算法在机器人足球比赛中的应用

针对机器人足球中寻路效率和多智能体协作问题,提出相应的解决方案,文章主要研究在机器人足球比赛中算法和策略问题。将蚁群算法应用到该比赛中,经实际比赛证明该算法在机器人足球比赛中对于提高机器人寻找足球和攻防性能方面都能得到很好的结果。     

产业集群和产业集群技术创新——基于缄默知识论

产业集群内部存在着显性知识和缄默知识两类知识.与显性知识相比,缄默知识是产业集群更重要的知识资源,是产业集群技术创新成功的一个不可或缺的变量,产业集群而产业集群有利于缄默知识的传播和扩散从而促进技术创新.     

转包型产业集群的长期交易及成因分析

文章在描述转包型产业集群的交易长期性和信任性的特点基础上，进而分析集群企业长期交易关系的形成、维持与强化的影响因素，揭示了长期交易发育发展既是集群自身的运行特性又是外部多种因素综合作用的必然结果。     

产业集群中校企合作因素分析

波特认为,区域之间的竞争优势实际上是产业集群的竞争。本文一方面对产业集群中校企合作的动力作了分析;另一方面分析了产业集群中校企合作的影响力;另外,提出了对未来产业集群中校企合作改革的几点建议。     

一种基于Levy-人工蜂群的三维Otsu阈值分割算法

针对图像分割过程中三维Otsu算法运算时间长、计算量大的问题,提出了一种基于Levy-人工蜂群算法的三维Otsu阈值分割算法。首先,以像素灰度值-邻域均值-邻域中值的三维类间方差作为人工蜂群算法的适应度函数;其次,采用Levy飞行模式评价像素的适应度,对其种群更新及邻域搜索过程进行优化,以增强其全局搜索能力;最后,利用改进后的算法得到的分割阈值对图像进行分割。仿真实验结果表明,与传统三维Otsu阈值分割算法相比,所提算法能够有效降低图像存储空间,处理时间降低了30.8%,具备更好的抗噪性能,分割效果也更为理想。     

基于IK-MD-SA聚类算法的电力数据审计疑点研究

为解决电力行业海量非结构化数据导致审计疑点数据效率、准确性低的难题,本文提出了一种基于迭代IK-MD-SA聚类电力大数据审计疑点算法。首先运用相异性度量算法通过构造相异性矩阵和计算均值相异性改进K-means聚类算法选择初始聚类中心,并将簇均值替换为簇中位数完成后续聚类中心迭代,以消离群点影响聚类结果的准确性。然后利用改进的蜂群算法对聚类结果进行优化,使其保证高运行效率的前提下聚类结果仍具有较高准确性。最后,通过离散性电力数据进行识别潜在疑点试验,验证了所提算法的可行性和有效性。     

支持长沙市应急物流配送的MapInfo开发

文章以面向对象程序设计语言Visual Basic为开发工具,借助MapInfo软件,集成MapX控件,实现了一个支持长沙市应急物流配送的MapInfo系统,利用改进蚁群算法进行路径优化,应用MATLAB软件验证了有效性和可行性。