Ant colony optimization and travelling salesman problem

Jiachen Huang

doi:10.1117/12.2628031

22 April 2022 Ant colony optimization and travelling salesman problem

Jiachen Huang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634K (2022) https://doi.org/10.1117/12.2628031
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Ant colony optimization is becoming an attractive hot spot, and it is always a highlight of major algorithms. Enormous advancements have been realized in the ACO algorithm, such as self-adaptive algorithm advancements, boosting the diversity of various groups, enhancing local search improvements. However, with advances in multicore computing theory and technology, implementing ACO has become a new challenge for all scholars. The ACO is a probabilistic algorithm that is used to find optimization routes. Marco Dorigo presented it in 1992, and it was based on the behaviors that ants engage in when searching for food. ACO is a type of simulated evolutionary algorithm that, according to prior study, has a number of advantages. Researchers in numerous sectors have recently been researching and paying more attention to the development of ACO. However, with advances in multicore computing theory and technology, implementing ACO efficiently and parallelly in a multicore computing environment emerges a key challenge in this field. Herein, this article will put forward exactly how ACO works and how ACO algorithm could be applied in travel problems to save time and money and provide the most efficient and safe trip. The result could be a reference; especially in holiday when the traffic reaches the most intensity.

Citation Download Citation

Jiachen Huang "Ant colony optimization and travelling salesman problem", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634K (22 April 2022); https://doi.org/10.1117/12.2628031

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