An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab, Ahmed A. Zaki; Abdul-Ghaffar, H.I.; Ahmed, Abdelsalam A.; Ramadan, Husam A.

doi:10.1049/rpg2.12359

Cited by 22 publications

(4 citation statements)

References 43 publications

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“…DLH search strategy improves the balance between local search and global search and keeps the diversity of population. In recent applied studies (Diab et al, 2022, Yesilbudark, 2021, Sales et al, 2021, IGWO's superiority in solving practical problems has been demonstrated. IGWO mainly consists of three stages: initialization (Step1), move (Step2), and select and update (Step3).…”

Section: Weight Optimization Algorithmmentioning

confidence: 99%

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Zhang

Kou

Ren

et al. 2022

Preprint

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Water shortage is a major problem facing the world. Artificial precipitation enhancement is an effective way to improve precipitation conversion rate, but the selection of artificial precipitation enhancement operation timing is the main difficulty, and the precipitable water vapor(PWV) is a major index. The variation of PWV is nonlinear and unstable due to complex factors, especially in the Qilian mountains in the northeastern part of the Qinghai-Tibet Plateau, so it is difficult to predict it accurately. Therefore, based on the analysis of the observed data of microwave radiometer in Qilian Mountains, a new combined model is constructed which considers both data decomposition and prediction of several single models in this research. In the data preprocessing stage, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is used to decompose and de-noise the PWV sequence. In the prediction stage, four neural network with unique characteristics, back propagation neural network (BPNN), long short term memroy (LSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN), are selected to predict the decomposed data respectively. A variant of gray Wolf optimization algorithm (IGWO) is used to determine the optimal weight of the model, and finally the comprehensive predicted value is obtained by weighting calculation. Through the analysis of experimental results, in the longest 15-step prediction, compared with CEEMDAN-BP, CEEMDAN-LSTM, CEEMDAN-BiGRU, CEEMDAN-TCN, the prediction accuracy can be improved by 54.17%, 35.05%, 22.38%, 23.86%, respectively. Other step size prediction also achieves the highest prediction accuracy.

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Section: Weight Optimization Algorithmmentioning

confidence: 99%

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Zhang

Kou

Ren

et al. 2022

Preprint

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“…PEMFC parameter estimation was done by developing Black Widow Optimization (BWO) along with the comparison of its results with other different meta-heuristic algorithms under various operating temperatures. Various other meta-heuristic algorithms that have been developed are the Slime Mould (SM)-based optimization algorithm, Modified Monarch Butterfly Optimization (MMBO) algorithm, Backtracking Search Algorithm combined with Burger's Chaotic map (BSABCM), Particle Swarm Optimization (PSO) [15], Genetic Algorithms (GA) [16], Chaotic Mayflies Optimization (CMO) algorithm [17], Grey Wolf Optimizer (GWO) [18], Whale Optimization Approach (WOA) [19], Salp Swarm Optimization algorithm (SSOA) [20], Effective Informed Adaptive PSO (EIA-PSO) [21], Cuckoo Search-Ant Colony Optimization (CS-ACO) [22], bi-inspired algorithm [23], Tribe PSO Algorithm (T-PSO) [24], Bi-Subgroup Optimization (BSO) [25], Sine Cosine Algorithm (SCA) [26], and Modified Owl Search Algorithm (DOSA) [27]. Another meta-heuristic algorithm, for example Puffer Fish (PF) [28], based on the behavior of male puffer fish in order to charm the female puffer fish by building special circular structures on the seabed, was developed for finding the optimal solution set to the problems of optimization.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

et al. 2023

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Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.

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“…Centroid opposition integrated with multiple strategies embedded on salp swarm algorithm can reduce the probability of the failure design system of reliability optimization [38]. An improved grey wolf optimizer with selective opposition shows efficiency for solving proton exchange membrane fuel cells 250W-stack [39]. The dynamic opposite generates mutual learning which is integrated with the mutation strategy for solving multi-task optimization problems [40].…”

Section: Introductionmentioning

confidence: 99%

Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

2022

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This paper presents the logical relationships of Aristotle's square of opposition on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by joining two opposition strategies of Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal Optimization (GJO) with Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluate the performance of GJO-JOS in a competition of CEC 2017 on a set of 29 benchmark functions. The benchmark functions include unimodal, multimodal, hybrid, and composition. Based on these benchmark functions, the evaluation results of GJO-JOS are competitive to GJO with seven single opposition-based learning strategies (OBLs). We also compare GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produces promising results among seven single OBLs, eight natured-inspired algorithms, and GJO. The experimental results confirm that GJO-JOS generates equilibrium in the balance mechanism effectively.

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An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Cited by 22 publications

References 43 publications

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

A Combined model based on data decomposition and multi-model weighted optimization for precipitable water vapor forecasting

Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

Golden Jackal Optimization With Joint Opposite Selection: An Enhanced Nature-Inspired Optimization Algorithm for Solving Optimization Problems

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