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A Parallel Framework for Multi-Population Cultural Algorithm and Its Applications in TSP

  • Olgierd UnoldEmail author
  • Radosław Tarnawski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

This paper presents a novel parallel framework based on the Multi-Population Cultural Algorithm (MPCA) scheme for optimization problems. Contrary to the existing variants of Cultural Algorithm (CA), the proposed parallel framework for MPCA (PFMPCA) allows the use of any implemented metaheuristic both in a belief, and in a population space. Furthermore, the proposed approach permits CA to evolve simultaneously multiple population and belief sub-spaces, leveraging the dual inheritance mechanism and utilizing multi-population approach. Moreover, each sub-population (in population or belief space) is able to communicate between each other. PFMPCA has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. The performance of the developed framework was evaluated using asymmetric Travelling Salesman Problem (ATSP). The MPCA for TSP implemented by means of the parallel framework proves to have an extensible architecture designed to accommodate changes and good performances.

Keywords

Cultural Algorithm Multi-Population GPU computing CUDA architecture Travelling Salesman Problem Ant Colony Optimization Genetic Algorithm 

Notes

Acknowledgments

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

Author Contribution

OU initiated and designed the study, supervised the work, made statistical tests. RT implemented the framework, performed the experiments. Both authors wrote and approved the final manuscript.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

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