基于粗粒度模型的蚁群优化并行算法  

Ant Colony Optimization Parallel Algorithm Based on Coarse-grained Model

作　　者：朱庆保[1] 

机构地区：[1]南京师范大学计算机系,南京210097

出　　处：《计算机工程》2005年第1期157-159,共3页Computer Engineering

基　　金：江苏省教育厅自然科学基金资助项目(2001SXXTSJB111)

摘　　要：为了改进蚁群优化算法的收敛速度,研究了一种基于粗粒度模型的并行蚁群优化算法,该算法将搜索任务划分给q个子群,由这些子群并行地完成搜索,可使搜索速度大幅度提高。实验结果表明,用该算法求解TSP问题,收敛速度比最新的改进算法快百倍以上。In order to improve the speed of convergence of ant colony optimization, a parallel algorithm based on coarse-grained model is proposed in the paper, search tasks are assigned to q ant subgroups ,and parallel searching are finished by q subgroups. Results of experiment show that the algorithm described in this paper makes the searching speed hundreds of times faster than the latest improved algorithm.

关 键 词：蚁群优化算法 蚁群系统 并行算法 粗粒度模型 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]