Research Area:  Metaheuristic Computing

Abstract:

In this paper, important functional parameters of solid oxide fuel cells are identified by introducing a novel high-speed optimization method, namely adaptive chaotic grey wolf optimization algorithm. The suggested optimization method is obtained by combining the adaptive grey wolf optimization and chaotic grey wolf optimization algorithms. The chaotic algorithm is applied to the basic grey wolf optimization to achieve higher convergence speed, keep the populations diversity, and provide an initial population with uniform distribution. Besides, a nonlinear convergence factor is defined for balancing the global and local exploration abilities. Employing the improved convergence factor resulted in a new version of the grey wolf optimization algorithm, namely adaptive grey wolf optimization algorithm. Adaptive chaotic grey wolf optimization algorithm adopts the advantages of both chaotic grey wolf optimization and adaptive grey wolf optimization methods simultaneously. The adaptive grey wolf optimization algorithm is applied to a 5 kW dynamic tubular stack. The results of the simulation report the lowest values of mean squared error, higher accuracy, higher robustness, and high convergence speed for the adaptive grey wolf optimization algorithm compared to some well-known optimization methods. Besides, the proposed method shows a good agreement with experimental results with lower computational difficulty.

Keywords:  
solid oxide fuel cells
chaotic grey wolf optimization
population diversity
mean squared error
accuracy
robustness
convergence speed

Author(s) Name:  Peng Hao, Behnam Sobhani

Publisher name:  ELSEVIER

DOI:  10.1016/j.ijhydene.2021.08.174

Volume Information:  Volume 46, Issue 73

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0360319921034194