Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems

  • Authors:

  • Hui Wang;Shahryar Rahnamayan;Zhijian Wu

  • Affiliations:

  • School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China;Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada;State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, PR China

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Solving high-dimensional global optimization problems is a time-consuming task because of the high complexity of the problems. To reduce the computational time for high-dimensional problems, this paper presents a parallel differential evolution (DE) based on Graphics Processing Units (GPUs). The proposed approach is called GOjDE, which employs self-adapting control parameters and generalized opposition-based learning (GOBL). The adapting parameters strategy is helpful to avoid manually adjusting the control parameters, and GOBL is beneficial for improving the quality of candidate solutions. Simulation experiments are conducted on a set of recently proposed high-dimensional benchmark problems with dimensions of 100, 200, 500 and 1,000. Simulation results demonstrate that GjODE is better than, or at least comparable to, six other algorithms, and employing GPU can effectively reduce computational time. The obtained maximum speedup is up to 75.