A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based...

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Main Authors: Zhi Zeng, Yuxing Cai, Kwok L. Chung, Hui Lin, Jinwei Wu
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:IET Computers & Digital Techniques
Online Access:http://dx.doi.org/10.1049/2023/9915769
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author Zhi Zeng
Yuxing Cai
Kwok L. Chung
Hui Lin
Jinwei Wu
author_facet Zhi Zeng
Yuxing Cai
Kwok L. Chung
Hui Lin
Jinwei Wu
author_sort Zhi Zeng
collection DOAJ
description In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP-ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed-up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system-roulette wheel method-bitmask tabu (MMAS-RWM-BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed-up ratio and less runtime, proving that the proposed FP-ACO is more suitable for solving TSP.
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issn 1751-861X
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publishDate 2023-01-01
publisher Wiley
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series IET Computers & Digital Techniques
spelling doaj-art-0c7224bf5844475092b7756ff65650ec2025-02-03T01:29:35ZengWileyIET Computers & Digital Techniques1751-861X2023-01-01202310.1049/2023/9915769A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSPZhi Zeng0Yuxing Cai1Kwok L. Chung2Hui Lin3Jinwei Wu4School of Computer Science and EngineeringSchool of Computer Science and EngineeringSchool of Computer Science and EngineeringCollege of Resources and EnvironmentSchool of Mathematics and StatisticsIn view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP-ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed-up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system-roulette wheel method-bitmask tabu (MMAS-RWM-BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed-up ratio and less runtime, proving that the proposed FP-ACO is more suitable for solving TSP.http://dx.doi.org/10.1049/2023/9915769
spellingShingle Zhi Zeng
Yuxing Cai
Kwok L. Chung
Hui Lin
Jinwei Wu
A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
IET Computers & Digital Techniques
title A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
title_full A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
title_fullStr A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
title_full_unstemmed A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
title_short A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP
title_sort fast fully parallel ant colony optimization algorithm based on cuda for solving tsp
url http://dx.doi.org/10.1049/2023/9915769
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