Ant colony optimization algorithm and its application

被引：1
|
作者：
Chen, Aoran [1 ]
Tan, Hao [2 ]
Zhu, Yiyue [3 ]
机构：
[1] Univ Minnesota Twin Cities, Dept Math, Minneapolis, MN 55455 USA
[2] Southwest Univ, Dept Westa, Chongqing 400000, Peoples R China
[3] Chongqing Univ Technol Chongqing, Dept Mech Engn, Chongqing 400000, Peoples R China
来源：
2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELLING, AND INTELLIGENT COMPUTING (CAMMIC 2022) | 2022年 / 12259卷
关键词：
Ant colony; ACO algorithm; TSP Problem; !text type='Python']Python[!/text; SCHEDULING PROBLEM;
D O I：
10.1117/12.2639584
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Ant Colony Optimization (ACO) algorithm is a bionic algorithm simulating ant colony behavior. Ant colony can find the shortest path by sensing pheromone when looking for food. Ant colony algorithm plays an important role in solving a variety of planning problems such as traveling salesman problem (TSP). This paper mainly introduces the background knowledge of ACO algorithm in entomology and computer and uses python implementation to solve an example of traveling salesman problem. This work focuses on the ACO algorithm, which was inspired by the ant colony system. Starting with pheromone direction, this study concentrates on the properties and principles of the ACO algorithm, and then applies the ACO algorithm to solve the TSP issue successfully. Pheromone is used by the entire ant colony as an indirect communication method to address difficult challenges.
引用
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页数：7
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