Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm

Abstract

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.

Optimal PEM Fuel Cell Model Using a Novel Circle Search Algorithm

“…In the literature review, many metaheuristic algorithms are utilized to find the optimal parameters of the PEMFC model, including the genetic algorithm (GA) [21], particle swarm optimization with neural networks [22], and evolutional and differential evolution algorithms [23,24] including the flower pollination algorithm [25], the harmony search algorithm [26], the neural network algorithm [27], the whale optimization algorithm [28], the Chaos game optimization algorithm [29], and the hunger game search algorithm [30]. Other metaheuristic algorithms are modified to improve their performance for PEMFC modeling, including an improved version of the Archimedes optimization algorithm [31], the chaotic binary shark smell optimization algorithm [32], an extended version of the crow search algorithm [33], hybrid sine cosine and crow search algorithms [34], a Harris hawks optimization algorithm [35], an improved salp swarm algorithm [36], an enhanced transient search optimization algorithm [37,38], and a developed arithmetic optimization algorithm [39].…”

“…Its maximum value is 23 in this paper. Actually, the value of factor λ is based on the relative humidity and other factors [30].…”

Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods

“…It is important to emphasize that the HGS is a very recent algorithm and has not been applied so far to predict drought according to the best of the authors' knowledge. However, the combination of HGS with other algorithms demonstrated that it enhances the computation performance in proton exchange membrane fuel cells [35], reduces the consumption [36], optimizes the blast patterns, and reduces the environmental effects [37] in the optimization of the photovoltaic models and manufacturing processes [38], among others.…”

An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

“…14 Consequently, over the past 10 years, researchers have focused on meta-heuristic methods to overcome issues in the PEMFC modeling. 15 To name some metaheuristics in their original version utilized to find PEMFC models' unknown parameters, they are particle swarm optimization (PSO), 16 hunger games search (HGS), 17 salp swarm optimizer (SSO), 18 satin bowerbird optimizer (SBO), 19 grey wolf optimizer (GWO), 20 multiverse optimizer (MVO), 21 slime mould algorithm (SMA), 22 atom search optimizer (ASO), 23 bird mating optimizer (BMO), 24 gradient-based optimizer (GBO), 25 and whale optimization algorithm (WOA). 26 In addition, a number of meta-heuristics developed by improving or hybridizing the original algorithms have been employed to address the studied problem, such as adaptive sparrow search algorithm (ASSA), 27 transferred adaptive differential evolution (TRADE), 28 developed coyote optimization algorithm (DCOA), 29 improved barnacles mating optimization (IBMO), 30 improved monarch butterfly optimization (IMBO), 31 improved salp swarm algorithm (ISSA), 32 improved artificial ecosystem optimizer (IAEO), 33 modified monarch butterfly optimization (MMBO), 34 modified gorilla troops optimizer (MGTO), 35 modified artificial electric field algorithm (mAEFA), 36 chaotic mayfly optimization algorithm (CMOA), 37 hybrid interior search algorithm (HISA), 38 hybrid vortex search algorithm and differential evaluation (HVSA-DE), 39 hybrid water cycle mouth-flame optimization (WCMFO), 40 and hybrid sine-cosine crow search algorithm (SCCSA).…”