Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models

Ridha, Hussein Mohammed; Heidari, Ali Asghar; Wang, Mingjing; Chen, Huiling

doi:10.1016/j.enconman.2020.112660

Cited by 178 publications

(67 citation statements)

References 65 publications

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“…Further, in work by [21], the authors improved the exploration phase of HHO for parameter identification of simulated annealing (SA) single-diode solar cell models. Additionally, In [32], the authors proposed to solve the job scheduling problem in cloud computing using modified HHO with a simulated annealing algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…It also uses the rabbit energy to prevent the hawks from falling in local optima. However, the rabbit escaping energy may rapidly change its convergence towards the optimal solution, which may cause the hawks to trapped in local optima [21]. In this subsection, we explain the proposed three search strategies (TSS) to enhance both of the global and local search mechanisms of the HHO algorithm.…”

Section: The Three Search Strategiesmentioning

confidence: 99%

“…Furthermore, IHHO was compared to the standard HHO and other state-of-the-art optimization algorithms such as GA, GOA, PSO, ALO, WOA, BOA and SMA. All the algorithms have been transferred to fit binary feature selection using Equation (21). TABLE II displays the general parameter settings for the utilized algorithms.…”

Section: Parameter Settingmentioning

confidence: 99%

“…Accordingly, HHO does not perform a global search in the second half of iterations, although the currently selected area may not be the optimal one [20]. Premature convergence is another issue where the Harris hawks (population) converge to a local solution instead of the global solution [21].…”

Section: Introductionmentioning

confidence: 99%

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Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost. Feature selection technique has a critical role as a preprocessing step in reducing these issues. It works by eliminating the features that may negatively influence the classifiers' performance, such as irrelevant, redundant and less informative features. This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite oppositebased learning and proposing a new search mechanism. Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems. Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities. Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate. To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikit-feature project. The datasets represent different levels of feature dimensionality, such as low, moderate, and high. Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests. Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed. The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.

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Section: Related Workmentioning

confidence: 99%

Section: The Three Search Strategiesmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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“…As the latest member of MEAs, HHO performs a simple mechanism and can be used easily. Since proposed [53], it has been used widely [54]- [56], such as solar energy [57]- [59], feature selection [60], drug design and discovery [61]. Furthermore, a large number of improved HHO variants have been presented, for example, hybrid HHO-based sine cosine mechanism [62], Nelder-mead driven HHO [63], generalized Gaussian distribution HHO [64], multi-objective HHO [65], mutation strategies-based HHO [66], diversification enriched HHO [58], Multi-population version [67] random forest model based-HHO [68].…”

Section: Proposed Sglhhomentioning

confidence: 99%

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

et al. 2020

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Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning. INDEX TERMS Di-2-Ethylhexyl phthalate; Hepatotoxicity; Support vector machine; Harris hawks optimization; Salp swarm algorithm; Grey wolf optimizer.

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Parameter Estimation of Solar Cells: A State‐of‐the‐Art Review with Metaheuristic Approaches and Future Recommendations

2020

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Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models

Cited by 178 publications

References 65 publications

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

Parameter Estimation of Solar Cells: A State‐of‐the‐Art Review with Metaheuristic Approaches and Future Recommendations

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