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Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

  • Olgierd UnoldEmail author
  • Radosław Tarnawski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

Ant Colony Optimization (ACO) is a well-established metaheuristic successfully applied to solve hard combinatorial optimization problems, including Travelling Salesman Problem (TSP). However, ACO algorithm as many population-based approaches has some disadvantages, such as lower solution quality and longer computational time. To overcome these issues, parallel Cultural Ant Colony Optimization (pCACO) is introduced in this paper. The proposed approach hybridises Cultural Algorithm with ACO-based \(\mathcal {MAX}\)-\(\mathcal {MIN}\) Ant System. pCACO has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. Through testing nine benchmark asymmetric TSP problems, the experimental results show the new method enhances the solution quality when compared to sequential and parallel ACO, yielding comparable computational time to parallel ACO.

Keywords

Travelling Salesman Problem Ant Colony Optimization Cultural Algorithm GPU computing CUDA architecture 

Notes

Acknowledgments

The work was supported by statutory grant of the Wroclaw University of Science and Technology, Poland.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of ElectronicsWroclaw University of Science and TechnologyWroclawPoland

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