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A GPU-Based jDE Algorithm Applied to Continuous Unconstrained Optimization

  • Mateus Boiani
  • Gabriel Dominico
  • Rafael Stubs ParpinelliEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallel architectures promoted by the use of GPUs. Hence, this paper proposes a GPU-based self-adaptive Differential Evolution employing the jDE mechanism to control its parameters, named cujDE. Two CUDA structures are employed to model the kernel functions in cujDE. The proposed algorithm is compared with another GPU-based self-adaptive DE (cuSaDE) in four continuous unconstrained benchmark functions. Also, the speedup between the CPU-based and the GPU-based jDE is measured. Results obtained suggest that the proposed approach is well suited to and competitive for continuous optimization.

Keywords

CUDA GPU computing Optimization Evolutionary algorithms Parallel computing 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mateus Boiani
    • 1
  • Gabriel Dominico
    • 1
  • Rafael Stubs Parpinelli
    • 1
    Email author
  1. 1.Graduate Program in Applied ComputingSanta Catarina State UniversityJoinvilleBrazil

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