Approximation performance of ant colony optimization for the TSP(1,2) problem

被引：5
|
作者：
Peng, Xue [1 ]
Zhou, Yuren [1 ,2 ]
Xu, Gang [3 ]
机构：
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Sch Adv Comp, Guangzhou 510275, Guangdong, Peoples R China
[3] Nanchang Univ, Dept Math, Nanchang 330031, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2016年 / 93卷 / 10期
基金：
中国国家自然科学基金;
关键词：
ant colony optimization; approximation performance; runtime analysis; TSP(1,2); NP-complete; RUNTIME ANALYSIS; ALGORITHM;
D O I：
10.1080/00207160.2015.1071359
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Ant colony optimization (ACO) is a kind of powerful and popular randomized search heuristic for solving combinatorial optimization problems. This paper investigates the performance of two ACO algorithms, called MMAS(Ord)* and MMAS(Arb)*, on the travelling salesman problem with distance one and two (TSP(1,2)) which is an NP-complete problem. It is shown that two ACO algorithms obtain an approximation ratio of 3/2 with regard to the optimal solutions in expected polynomial runtimes. We also study the influence of pheromone information and heuristic information on the approximation performance. Finally, we construct an instance and demonstrate that ACO outperforms the local search algorithms on this instance.
引用
收藏
页码：1683 / 1694
页数：12
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