Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer

Shehabeldeen, Taher A.; Elaziz, Mohamed Abd; Elsheikh, Ammar H.; Zhou, Jianxin

doi:10.1016/j.jmrt.2019.09.060

Cited by 99 publications

(32 citation statements)

References 55 publications

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“…This value is used to indicate either the physical flagging of the prey for −1 ≤ E 0 < 0 or its strengthening 0 ≤ E 0 < 1. Furthermore, in the case ∣ E ∣ ≥ 1, then HHOA will explore the search space otherwise, HHOA will change its status to the exploitation phase 53 …”

Section: Harris Hawks Optimization Algorithmmentioning

confidence: 99%

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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

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

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Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection. Nature‐inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, the opposition‐based learning Harris hawks optimization algorithm (HHOA‐OBL) is proposed to optimize the hyperparameters of the v‐SVR with embedding the feature selection simultaneously. The experimental results over four datasets show that the HHOA‐OBL outperforms the standard Harris hawks optimization algorithm, grid search, and cross‐validation methods, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the HHOA‐OBL confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared with other nature‐inspired algorithms, which proves the ability of HHOA‐OBL in searching for the best hyperparameters values and selecting the most informative features for prediction tasks. Thus, the experiments and comparisons support the performance of the proposed approach in making predictions in other real applications.

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Section: Harris Hawks Optimization Algorithmmentioning

confidence: 99%

“…Furthermore, in the case jE j ≥ 1, then HHOA will explore the search space otherwise, HHOA will change its status to the exploitation phase. 53…”

Section: Transition Phasementioning

confidence: 99%

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

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

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“…Artificial neural networks (ANN) are powerful information processing paradigms that mimic the human brain in processing data. ANNs have been employed to model different engineering problems ( Babikir et al, 2019 ; Elsheikh et al, 2020b ; Shehabeldeen et al, 2019 ). ANN has a number of advantages over other traditional modeling approaches such as handling enormous amounts of data, generalization capabilities, identifying complex relationships between dependent and independent variables, and detecting the inherent interactions between process variables ( Elaziz et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

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119

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“…Heidari et al [36] demonstrated that HHO outperforms nature-inspired techniques in 29 engineering problems. It had been used in various applications, such as feature selection [37], engineering problems [38][39][40][41][42][43], satellite image processing [44], prediction models [45], and scheduling tasks in cloud computing [46]. SSA is also a nature-inspired method that simulates the behavior of Salpidae's family.…”

Section: Introductionmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

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Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer

Cited by 99 publications

References 55 publications

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

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

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

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