Comparison of Ant Colony Optimization Algorithms for Small-Sized Travelling Salesman Problems

被引：0

作者：

Subaskaran, Arcsuta ^{[1
]}

Krahemann, Marc ^{[1
]}

Hanne, Thomas ^{[1
]}

Dornberger, Rolf ^{[1
]}

机构：

[1] Univ Appl Sci & Arts Northwestern Switzerland, Muttenz, Olten, Switzerland

来源：

INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021 | 2022年 / 419卷

关键词：

Ant Colony Optimization; Travelling Salesman Problem; Improved Ant Colony Optimization; Comparison; Metaheuristics;

D O I：

10.1007/978-3-030-96299-9_2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with Ant Colony Optimization (ACO) applied to the Travelling Salesman Problem (TSP). TSP is a well-known combinatorial problem which aim is to find the shortest path between a designated set of nodes. ACO is an algorithm inspired by the natural behavior of ants. When travelling from the nest to a food source, ants leave pheromones behind. This algorithm can be applied to TSP in order to find the shortest path. In this paper, the variants of ACO are shortly explained and a new Improved Ant Colony Optimization (TACO) algorithm is proposed. The TACO is applied to small-sized TSP. It is shown that the proposed TACO performs better in some cases, especially when there are more cities in the TSP.

引用

页码：15 / 23

页数：9

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