On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Abbasi, Ahmad; Firouzi, Behnam; Şendur, Polat

doi:10.1007/s00366-019-00892-0

Cited by 66 publications

(28 citation statements)

References 49 publications

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“…Moreover, Harris Hawks Optimization (HHO) algorithm has been applied for extracting the parameters of the PEMFC model in Refs. [31][32][33][34]. The obtained results in the reported references [31][32][33][34] prove the ability of the HHO to estimate the parameters of PEMFC.…”

Section: Introductionsupporting

confidence: 59%

“…[31][32][33][34]. The obtained results in the reported references [31][32][33][34] prove the ability of the HHO to estimate the parameters of PEMFC. A comparison between the reported results from references [31][32][33][34] and those of the MPA and PO algorithms will be presented in the results section to evaluate the effectiveness of each algorithm.…”

Section: Introductionsupporting

confidence: 59%

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Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

et al. 2020

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Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability. INDEX TERMS Fuel cell modelling, Parameter estimation, metaheuristic algorithms.

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Section: Introductionsupporting

confidence: 59%

Section: Introductionsupporting

confidence: 59%

Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

et al. 2020

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“…Ones of the m ain ideas were particle swarm optimizer (PSO) [37], whale optimizer (WOA) [38]- [40], teaching-learning based optimizer (TLBO) [41], grasshopper optimizer (GOA) [42], [43], bacterial foraging optimization (BFO) [44], [45], gray wolf optimizer (GWO) [46], [47], fruit fly optimizer (FOA) [48]- [50], moth-flame optimization (MFO) [51], [52], and Harris hawks optimizer (HHO) [53]. Recently, HHO finds its applications in several types of problems [54] including micro-channel heat sink design [55], optimization of related components of harmonics polluted distribution systems [56], satellite image de-noising [57], manufacturing optimization problems [58], structural design optimization of vehicle components [59], drug design and discovery [60], and soil compression coefficient prediction [61]. Still, many real-life cases not modeled as an optimization case, whereas there are some aspects that we can solve those cases using new optimizers such as HHO [62]- [65].…”

Section: Proposed Gbhhomentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

et al. 2020

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This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students. The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk's optimizer (HHO). In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets. Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students. In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems. The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems. At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention. The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics. Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.

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“…The results of HHO algorithm report that it is very effective with meta -heuristic optimizers and show superiority over conventional techniques. Several authors used HHO algorithm for various applications in engineering [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] . The performance of HHO algorithm has been tested with other optimizers in [22] .…”

Section: Related Literaturementioning

confidence: 99%

“…The performance of HHO algorithm has been tested with other optimizers in [22] . It has been successfully used for the design of microchannel heat sinks [38] , for solving the coordination problem in the directional over current relays (DOCRs) [39] , for extracting the unknown parameters of the three-diode photovoltaic (TDPV) model [40] , for manufacturing optimization problems [41] , for accurate and reliable air pollutant forecasting model [42] , for solving reference point multi-objective problems [43] , for image processing [44] , for satellite image segmentation [45] etc. In [46] , the authors proposed hybrid models by combining artificial neural network (ANN) with GOA and HHO for predicting soil compression coefficient (SCC).…”

Section: Related Literaturementioning

confidence: 99%