Routing Design and Optimization Based on the Improved Ant Colony Algorithm

DOI

10.2991/978-94-6463-300-9_27How to use a DOI?

Keywords

Route design; colony algorithm; optimization

Abstract

This study aims to address the practical problem of providing an optimal and cost-effective short-distance route for tourists by employing an enhanced version of the Ant Colony Optimization algorithm (ACO). The research begins by presenting a comprehensive overview of the evolution of ACO algorithms. Subsequently, the focus shifts towards the application of Ant System (AS) and Max-Min Ant System (MMAS) to solve the Traveling Salesman Problem (TSP) and ACO to address the Quadratic Assignment Problem (QAP). Through a comparative analysis conducted on multiple datasets, it is determined that MMAS outperforms AS in terms of convergence speed, stability, and the attainment of optimal solutions. The algorithm is validated using publicly available TSP and QAP datasets, thereby confirming its feasibility. Subsequently, the TSP and QAP problems are integrated, and the improved algorithm is applied to the merged problem. Through the practical scenario of visiting the scenic spots in Zhuhai City in China, the effectiveness of the enhanced algorithm in solving the merged TSP and QAP problem is demonstrated. Experimental results demonstrate the capability of the improved algorithm to effectively solve the merged TSP and QAP problem. Furthermore, when applied to the practical problem of visiting Zhuhai's scenic spots, the improved algorithm proposes a rational route that fulfills the objective.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jingxuan Yang
PY  - 2023
DA  - 2023/11/27
TI  - Routing Design and Optimization Based on the Improved Ant Colony Algorithm
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 264
EP  - 279
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_27
DO  - 10.2991/978-94-6463-300-9_27
ID  - Yang2023
ER  -