Ant colony optimization algorithm and its application

Aoran Chen; Hao Tan; Yiyue Zhu

doi:10.1117/12.2639584

17 May 2022 Ant colony optimization algorithm and its application

Aoran Chen, Hao Tan, Yiyue Zhu

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122595O (2022) https://doi.org/10.1117/12.2639584
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

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.

Citation Download Citation

Aoran Chen, Hao Tan, and Yiyue Zhu "Ant colony optimization algorithm and its application", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122595O (17 May 2022); https://doi.org/10.1117/12.2639584

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