Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Ismael, Omar; Qasim, Omar Saber; Algamal, Zakariya Yahya

doi:10.1002/cem.3311

Cited by 19 publications

(9 citation statements)

References 65 publications

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“…The research of Ismael et al (2020Ismael et al ( , 2021 proposes HHO to optimize v-SVR hyperparameters by embedding feature selection simultaneously or without separating the process. This study also uses only a few chemical datasets to see the performance of the proposed method.…”

Section: Discussionmentioning

confidence: 99%

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Using Harris hawk optimization towards support vector regression to ozone prediction

Kurniawan¹,

Setiawan

Caraka

et al. 2022

Stoch Environ Res Risk Assess

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As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimizationsupport vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R 2 ), Variance Ratio (VR), and Diebold-Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R 2 around 0.97, and VR around 0.98. Then, the results of the Diebold-Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.

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Section: Discussionmentioning

confidence: 99%

“…There are several related studies that have existed before. Ismael et al (2020Ismael et al ( , 2021 propose HHO to optimize v-SVR hyperparameters by embedding feature selection simultaneously or without separating the process. But in the evaluation of the model using only MSE.…”

Section: Introductionmentioning

confidence: 99%

Using Harris hawk optimization towards support vector regression to ozone prediction

Kurniawan¹,

Setiawan

Caraka

et al. 2022

Stoch Environ Res Risk Assess

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“…To perform segmentation on satellite and oil pollution images [23] Hybrid HHO differential evolution Making two equal subpopulations from a complete one and training both the subpopulation parallelly using HHO and differential evolution Multilevel image segmentation [24] Adaptive HHO Mutation is used by HHO to clip the escape energy Multilevel image segmentation [25] Hybrid OBL-HHO OBL generates a solution for HHO through adversarial learning approach…”

Section: Fuzzy Harris Hawk Algorithmmentioning

confidence: 99%

“…HHO has been used extensively for the feature selection process to optimize the parameters for classification methods [ 69 , 70 ]. Ismael et al [ 25 ] proposed to improve the HHO method by employing an opposition-based learning approach (OBL). The OBL generates a solution for meta-heuristic algorithms through an adversarial learning approach.…”

Section: Applications Of Hhomentioning

confidence: 99%

Harris Hawk Optimization: A Survey onVariants and Applications

Tripathy

Maddikunta

Pham

et al. 2022

Computational Intelligence and Neuroscience

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In this review, we intend to present a complete literature survey on the conception and variants of the recent successful optimization algorithm, Harris Hawk optimizer (HHO), along with an updated set of applications in well-established works. For this purpose, we first present an overview of HHO, including its logic of equations and mathematical model. Next, we focus on reviewing different variants of HHO from the available well-established literature. To provide readers a deep vision and foster the application of the HHO, we review the state-of-the-art improvements of HHO, focusing mainly on fuzzy HHO and a new intuitionistic fuzzy HHO algorithm. We also review the applications of HHO in enhancing machine learning operations and in tackling engineering optimization problems. This survey can cover different aspects of HHO and its future applications to provide a basis for future research in the development of swarm intelligence paths and the use of HHO for real-world problems.

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“…e mean values of F10 and F11 are slightly superior, and the stability is also better. is section compares CCCHHO with THHO [33], MHHO [34], and OBLHHO [35]. We record the optimal value (best), mean value (avg), worst value (worst), and standard deviation (std) of each algorithm, and the results of each algorithm are shown in Table 3.…”

Section: Comparison Of Ccchho and Other Optimizationmentioning

confidence: 99%

Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance

Wen

Wang

Yue

et al. 2022

Discrete Dynamics in Nature and Society

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Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness.

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Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Cited by 19 publications

References 65 publications

Using Harris hawk optimization towards support vector regression to ozone prediction

Using Harris hawk optimization towards support vector regression to ozone prediction

Harris Hawk Optimization: A Survey onVariants and Applications

Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance

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