Developing and Applying Chaotic Harris Hawks Optimization Technique for Extracting Parameters of Several Proton Exchange Membrane Fuel Cell Stacks

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris' Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon's statistical test (P-value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

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“…Also, they used HHO as a local search operator within MVO to solve its local optima problem. Furthermore, in [42], they replaced the random…”

Section: B Chaotic Mapsmentioning

confidence: 99%

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

et al. 2020

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“…Table 4 presents the approximated value of current and voltage using the Bézier curve method compared with experimental data. The calculated IAE of the Bézier curve, CHHO [49], and MAEO [51] are compared and reported in Table 5. The purpose of the comparison is to validate the accuracy of the proposed technique in the characteristic curves process with other algorithms.…”

Section: Case Study 4: 250 W Pemfc Stackmentioning

confidence: 99%

“…To check the efficiency of the used technique and the performance of the obtained results, a statistical analysis is conducted to assess the precision and accuracy of the approximated V-I and P-I curves. The results obtained are compared to latest methods, such as the Whale Optimization Algorithm (WOA) [14], GRG-1 [22], GRG-2 [22], FOA [25], ICA [25], SFLA [25], SMSA [26], SSO [32], Modified Artificial Ecosystem Optimizatio (MAEO) [49], GA [38], MRFO [38], Grouping-based Global Harmony Search GGHS [50], Improved Harmouny Search (HIS) [50], Particle Swarm Optimization with adaptive inertia Weight (PSO-w) [50], and Chaotic Harris Hawks Optimization (CHHO) [51]. To evaluate the performance of the proposed method, various error parameters are computed, examined, and compared, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), and Autocorrelation Function (ACF).…”

Section: Introductionmentioning

confidence: 99%

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

2020

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Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bézier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12 W stack, NedStackPS6, and 250 W proton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein–Bézier function and de Casteljau’s algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bézier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bézier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques.

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“…The differential evaluation (DE), as well as the hybrid adaptive differential evaluation (HADE) algorithms, have been introduced for solving the optimization problem presented in [22], [23]. More recent optimization methods have been applied to solve the problem of PEMFC's parameter such as: the harmony search algorithm (HAS) [24], the seeker optimization algorithm (SOA) [25], the multi-verse optimizer (MVO) [26], the adaptive RNA genetic algorithm [27], Eagle strategy based on JAYA algorithm and Nelder-Mead simplex method (JAYA-Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models NM) [28], grey wolf optimizer (GWO) [29], hybrid Teaching Learning Based Optimization -Differential Evolution algorithm (TLBO-DE) [30], shark smell optimizer (SSO) [25], Cuckoo search algorithm with explosion operator (CS-EO) [31], selective hybrid stochastic strategy [32], bird mating optimizer [33], grasshopper optimizer (GHO) [34], Chaotic Harris Hawks optimization (CHHO) [35], and Modified Artificial Ecosystem Optimization (MAEO) [36].…”

Section: Introductionmentioning

confidence: 99%

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

Kamel¹,

Jurado²,

Sultan³

et al. 2020

IJIMAI

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Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem.

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Developing and Applying Chaotic Harris Hawks Optimization Technique for Extracting Parameters of Several Proton Exchange Membrane Fuel Cell Stacks

Cited by 99 publications

References 21 publications

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

Tree Growth Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell Models

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