An Improved Unordered Pair Bat Algorithm for Solving the Symmetrical Traveling Salesman Problem

被引：1

作者：

Zhang Nan ^{[1
]}

Lv Zhimin ^{[1
]}

Qiao Shen ^{[1
]}

Li Ting ^{[2
]}

机构：

[1] Univ Sci & Technol Beijing, Collaborat Innovat Ctr Steel Technol, 30 Xueyuan Rd, Beijing, Peoples R China

[2] Beijing Jinghang Res Inst Comp & Commun, Beijing, Peoples R China

来源：

FOUNDATIONS OF COMPUTING AND DECISION SCIENCES | 2022年 / 47卷 / 01期

基金：

中国国家自然科学基金;

关键词：

symmetrical traveling salesman problem; discrete bat algorithm; unordered pair bat algorithm; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; SEARCH ALGORITHM; SYSTEM;

D O I：

10.2478/fcds-2022-0004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bat algorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can't solve discrete optimization problems directly. Therefore, this paper introduces an unordered pair and proposes an unordered pair bat algorithm (UPBA) to make it more suitable for solving symmetric discrete traveling salesman problems. To verify the effectiveness of this method, the algorithm has been tested on 23 symmetric benchmarks and compared its performance with other algorithms. The results have shown that the proposed UPBA outperforms all the other alternatives significantly in most cases.

引用

页码：87 / 103

页数：17