Harris hawks optimization: Algorithm and applications

Heidari, Ali Asghar; Mirjalili, Seyedali; Faris, Hossam; Aljarah, Ibrahim; Mafarja, Majdi; Chen, Huiling

doi:10.1016/j.future.2019.02.028

Cited by 3,556 publications

(1,691 citation statements)

References 84 publications

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“…Also, despite the fact that previous studies have demonstrated the superiority of intelligent models (to traditional approaches) in terms of timeeffectiveness, the significance of time as a source in engineering works drives us to constantly seek more efficient models. Figure 10 shows the comparison between the computation time (and the obtained objective function) of the proposed ALO-ANN and some well-known optimization techniques, namely the dragonfly algorithm (DA) [54], Harris hawks optimization (HHO) [55], artificial bee colony (ABC) [56], imperialist competitive algorithm (ICA) [57], elephant herding As well as the error criteria, the R 2 is used to measure the consistency of the results in terms of the correlation. Figure 9 shows the regression charts of the training and testing data of the implemented models.…”

Section: Quality Assessment Of Predictive Modelsmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Quality Assessment Of Predictive Modelsmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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“…However, in recent years, the metaheuristic optimization algorithm has become very suitable for such problems, due to its fast convergence on an approximate optimal solution. In this paper, we use the Harris Hawk optimization algorithm proposed by Mirjalili et al [34]. The Harris Hawk optimization algorithm has been successfully applied to many practical applications due to its complete attack strategy and has achieved good results [35][36][37], but there are still some disadvantages that make it subject to local problems.…”

Section: Improve the Constrained Dna-sequence Lower Bound's Methodsmentioning

confidence: 99%

“…The Harris hawks optimization algorithm (HHO) is a population-based metaheuristic optimization algorithm that was proposed by Mirjalili et al [34]. Its main inspiration came from the cooperative predation between the hawks in the Harris area of the United States and the different chasing strategies for prey, also referred to as the 'surprise pounce'.…”

Section: The Original Algorithm (Hho)mentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

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“…Third, we investigated the performance of the dynamic adjusting parameter mechanism in the DANGHS algorithm only. In the future, we could investigate the performance of combining different dynamic adjusting parameter mechanisms into other metaheuristic algorithms, such as teaching learning based optimization (TLBO) [38], modified coyote optimization algorithm (MCOA) [39], Harris hawks optimization (HHO) [40], etc.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners

2019

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Commutation is a judicial policy that is implemented in most countries. The recidivism rate of commuted prisoners directly affects people’s perceptions and trust of commutation. Hence, if the recidivism rate of a commuted prisoner could be accurately predicted before the person returns to society, the number of reoffences could be reduced; thereby, enhancing trust in the process. Therefore, it is of considerable importance that the recidivism rates of commuted prisoners are accurately predicted. The dynamic adjusting novel global harmony search (DANGHS) algorithm, as proposed in 2018, is an improved algorithm that combines dynamic parameter adjustment strategies and the novel global harmony search (NGHS). The DANGHS algorithm improves the searching ability of the NGHS algorithm by using dynamic adjustment strategies for genetic mutation probability. In this paper, we combined the DANGHS algorithm and an artificial neural network (ANN) into a DANGHS-ANN forecasting system to predict the recidivism rate of commuted prisoners. To verify the prediction performance of the DANGHS-ANN algorithm, we compared the experimental results with five other forecasting systems. The results showed that the proposed DANGHS-ANN algorithm gave more accurate predictions. In addition, the use of the threshold linear posterior decreasing strategy with the DANGHS-ANN forecasting system resulted in more accurate predictions of recidivism. Finally, the metaheuristic algorithm performs better searches with the dynamic parameter adjustment strategy than without it.

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Harris hawks optimization: Algorithm and applications

Cited by 3,556 publications

References 84 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners

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