•  
  •  
 

Journal of System Simulation

Abstract

Abstract: Heuristic optimization algorithm are a type of algorithm that uses large-scale populations for iterative calculations and are widely used to solve all kinds of complex optimization problems. However, such algorithm have the disadvantages of large calculation and long time consumption. To solve this problem, heuristic optimization algorithms are parallelized using GPU and compute unified device architecture (CUDA) to substantially improve computational efficiency. A GPU parallel framework for heuristic optimization algorithm is proposed, which designs an information interaction framework and algorithm parallel optimization strategy with a parallel logical structure, and solves the problem of the dissimilarity of the logical structure of information interaction in series and parallel, this framework can parallelize various heuristic optimization algorithms with generality and efficiency. In order to verify the effectiveness of this framework, five common heuristic optimization algorithms are parallelized by using the parallel framework, and the comparison results of GPU parallel computation and CPU serial computation under different multiple test functions are given. in which DE, HHO, GWO, and WOA reach the acceleration ratio of 179.1, 178.6, 74.3 and 358.2 times respectively when the population dimension is 5000, while ensuring the accuracy of the results, which verifies the high effectiveness and practicability of the designed parallel framework.

First Page

1929

Last Page

1943

CLC

TP391.9

Recommended Citation

Wang Dongjie, Wen Sixin, Meng Wanzhi, et al. GPU Parallel Acceleration Framework for Heuristic Optimization Algorithm[J]. Journal of System Simulation, 2024, 36(8): 1929-1943.

Corresponding Author

Wen Sixin

DOI

10.16182/j.issn1004731x.joss.23-0780

Download

Share

COinS