并行蚁群算法及其在区位选址中的应用.pdfVIP

第39 卷 第3 期 测 绘 学 报 Vol.39, No.3 2010 年6 月 Acta Geodaetica et Cartographica Sinica Jun. ,2010 :10012 1595(2010)06 * 赵 元, 张新长 , 康停军 中山大学 地理科学与规划学院, 广东 广州 5 10275 A Parallel Ant Colony Optimization Algorithm for Site Location ZHAO Yuan, ZHANG Xinchang, ANG Tingjun School of Geography and Planning, Sun Yat2sen University, Guangzhou, 510275, China Abstract :An improved parallel ant colony optimization based on multiway tree is introduced to solve p 2median site location problem. To take advantage of ant colony optimization and GPU parallel computing, the ra ster spa ce is divided by the multiway tree and the ant paths are constructed on the nested subspace. An ideal solution can be obtained by the indirect communication of pheromone quickly. The study area is located in Guangzhou city, a densely populated region. This optimization problem considers the condition of population distribution and spatial 2 distance. The raster layers have a resolution of 92@92 m with a size of 512@5 12 pixels. A comparison experiment is conducted between the multiway tree ACO and simple search algorithms. Experiments indicate that this multiway tree ACO method can produce similar results but use lesser computation time, have better performance in conver2 gence precision compared with the simple search algorithms. In conclusion, the proposed algorithm is important and suitable for solving site search problems. ey words : multiway tree; ant colony optimization; parallel computation; site location; GPU general purpose computation :提出基于多叉树并行蚁群算法的区位选址优化方法。算法依据蚁群算法 有的并行特性,采用 GPU(graphic processing unit, 图形处理器) 并行运算技术,对地理空间进行多叉树划分, 收集蚂蚁在多叉树层间旅行时逐步留下的信 息素信息,进行路径选优获得理想的候选解, 从而为解决平面空间资源优化配置问题提供新的思路。实验结果表明, 与 普通蚁群算法相比,采用基于多叉树有哪些信誉好的足球投注网站的并行蚁群算法, 能够发