| Issue |
RAIRO-Oper. Res.
Volume 59, Number 2, March-April 2025
|
|
|---|---|---|
| Page(s) | 1199 - 1213 | |
| DOI | https://doi.org/10.1051/ro/2025031 | |
| Published online | 25 April 2025 | |
The adaptive two-stage ant colony Simulated Annealing Algorithm for solving the Traveling Salesman Problem
School of Automation, Beijing Information Science and Technology University, Beijing, P.R. China
* Corresponding author: wuyingnian@126.com
Received:
29
April
2024
Accepted:
24
March
2025
In the process of solving the Traveling Salesman Problem (TSP), both the Ant Colony Optimization and Simulated Annealing Algorithm exhibit different limitations depending on the dataset. This paper aims to address these limitations by Using the Ant Colony Optimization as a search strategy for the Simulated Annealing algorithm and designs two adaptive search stages based on the search characteristics of the Simulated Annealing algorithm. Thus solving the problem of slow convergence speed and easy getting stuck in local optimal solutions in the Simulated Annealing algorithm. By conducting tests on various TSPLIB datasets, the algorithm proposed in this article demonstrates improved convergence speed and solution quality compared to traditional algorithms. Furthermore, it exhibits certain advantages over other existing improved algorithms.
Mathematics Subject Classification: 90C27 / 90C59
Key words: Ant colony optimization / simulated annealing algorithm / algorithm improvement / traveling salesman problem
© The authors. Published by EDP Sciences, ROADEF, SMAI 2025
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