Efficiency improvement of ant colony optimization in solving the moderate LTSP

作     者：Munan Li 

作者机构：School of Business Administration South China University of Technology 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2015年第26卷第6期

页      面：1301-1309页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Fundamental Research Funds for the Central Universities(2015XZD15) the Soft Science Research Project of Guangdong Province(2015A070704015) the Guangdong Province Key Laboratory Open Foundation(2011A06090100101B) the Technology Trading System and Science&Technology Service Network Construction Project of Guangdong Province(2014A040402003) 

主　　题：ant colony optimization (ACO) travelling salesmanproblem (TSP) time-complexity of algorithm pheromone-trail up-dating. 

摘      要：In solving small- to medium-scale travelling salesman problems （TSPs） of both symmetric and asymmetric types, the traditional ant colony optimization （ACO） algorithm could work well, providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly challenged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advantages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is especially applicable in solving the moderate large-scale TSPs based on ACO.