Collaborative Attention Ant Colony Optimization for Large-Scale Traveling Salesman Problems
- 1School of Intelligent Systems Science and Engineering/JNU-Industry School of Artificial Intelligence, Jinan University, Guangzhou 510006, China
- 2ERICA Campus, Hanyang University, Ansan 15588, Republic of Korea
Abstract
Collaborative Attention Ant Colony Optimization (CAACO) integrates a deterministic coordinate-based attention prior with subpath decomposition, memetic MAX–MIN Ant System search, and double-bridge perturbation for the symmetric Euclidean Traveling Salesman Problem. The attention prior combines coordinate-direction compatibility and distance information to guide candidate selection without model training. Random-offset slicing organizes endpoint-constrained subpath search, while local improvement and perturbation refine and diversify the tour. An exploratory comparison of Full CAACO and CAACO-KNN uses 30 recorded runs per configuration on each of eight EUC_2D benchmark instances. Average instance-level mean GAP values are 2.945% and 3.222%, respectively, with a lower mean for Full CAACO on six instances. A two-sided exact Wilcoxon signed-rank test over the eight instance means gives p=0.0391; four per-instance comparisons remain significant after Holm correction. The results characterize configuration-level performance on the selected benchmarks and motivate further controlled evaluation of attention-guided search.
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BibTeX
@article{zhang2026collaborative,
title = {{Collaborative Attention Ant Colony Optimization for Large-Scale Traveling Salesman Problems}},
author = {Kexin Zhang and Shubin Liu and Xinyuan Zhang and Jun Zhang},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14203815},
url = {https://www.mdpi.com/2227-7390/14/20/3815},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Collaborative Attention Ant Colony Optimization for Large-Scale Traveling Salesman Problems
AU - Kexin Zhang
AU - Shubin Liu
AU - Xinyuan Zhang
AU - Jun Zhang
PY - 2026
DA - 2026/10/20
JO - Mathematics
DO - 10.3390/math14203815
UR - https://www.mdpi.com/2227-7390/14/20/3815
AB - Collaborative Attention Ant Colony Optimization (CAACO) integrates a deterministic coordinate-based attention prior with subpath decomposition, memetic MAX–MIN Ant System search, and double-bridge perturbation for the symmetric Euclidean Traveling Salesman Problem. The attention prior combines coordinate-direction compatibility and distance information to guide candidate selection without model training. Random-offset slicing organizes endpoint-constrained subpath search, while local improvement and perturbation refine and diversify the tour. An exploratory comparison of Full CAACO and CAACO-KNN uses 30 recorded runs per configuration on each of eight EUC_2D benchmark instances. Average instance-level mean GAP values are 2.945% and 3.222%, respectively, with a lower mean for Full CAACO on six instances. A two-sided exact Wilcoxon signed-rank test over the eight instance means gives p=0.0391; four per-instance comparisons remain significant after Holm correction. The results characterize configuration-level performance on the selected benchmarks and motivate further controlled evaluation of attention-guided search.
SN - 2227-7390
ER -