Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

Hashim, Fatma A.; Houssein, Essam H.; Hussain, Kashif; Mabrouk, Mai S.; Al-Atabany, Walid

doi:10.1016/j.matcom.2021.08.013

Cited by 574 publications

(147 citation statements)

References 37 publications

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“…r 1 ∈ [0,1] refers to a random number. Followed by [ 57 ], the exploration (Digging) and exploitation (Honey) are balanced using the density factor ( α ) that is defined as

where C > 1 stands for a constant value, T represents the total number of iterations, and t indicates the current iteration.…”

Section: Proposed Feature Selection Methodsmentioning

confidence: 99%

“…In addition, these MHT cannot solve all problems with the same efficiency according to the no free lunch theorem [ 56 ]. Therefore, this motivated us to propose an alternative FS method that depends on a recent efficient MHT called honey badger optimization (HBO) [ 57 ] algorithm that simulates the behaviour of honey badger to catch its prey. According to the mathematical model of these behaviours, HBO has been applied to solve global optimization problem and engineering problems [ 57 ].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this motivated us to propose an alternative FS method that depends on a recent efficient MHT called honey badger optimization (HBO) [ 57 ] algorithm that simulates the behaviour of honey badger to catch its prey. According to the mathematical model of these behaviours, HBO has been applied to solve global optimization problem and engineering problems [ 57 ]. In addition, we developed an extension of RST, named containment neighborhoods RSA (CRSA), as fitness value.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

Hosny

Elaziz

Ibrahim

2022

Computational Intelligence and Neuroscience

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This article appoints a novel model of rough set approximations (RSA), namely, rough set approximation models build on containment neighborhoods RSA (CRSA), that generalize the traditional notions of RSA and obtain valuable consequences by minifying the boundary areas. To justify this extension, it is integrated with the binary version of the honey badger optimization (HBO) algorithm as a feature selection (FS) approach. The main target of using this extension is to assess the quality of selected features. To evaluate the performance of BHBO based on CRSA, a set of ten datasets is used. In addition, the results of BHOB are compared with other well-known FS approaches. The results show the superiority of CRSA over the traditional RS approximations. In addition, they illustrate the high ability of BHBO to improve the classification accuracy overall the compared methods in terms of performance metrics.

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“…r 1 ∈ [0,1] refers to a random number. Followed by [ 57 ], the exploration (Digging) and exploitation (Honey) are balanced using the density factor ( α ) that is defined as

where C > 1 stands for a constant value, T represents the total number of iterations, and t indicates the current iteration.…”

Section: Proposed Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

Hosny

Elaziz

Ibrahim

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…In addition, F1-11 is tested in different dimensions to verify the optimization ability of the algorithm in high-dimensional cases. To prove that IMRFO is competitive, seven algorithms including MRFO, Honey Badger Algorithm (HBA) [ 45 ], GWO, PSO, Whale Optimization Algorithm (WOA) [ 46 ], Learning Based Optimization (TLBO) [ 47 ], and Flower Pollution Algorithm (FPA) [ 48 ] are compared. The new cluster intelligence algorithm was proposed by HBA in 2021, while other algorithms are classical ones that have been extensively studied.…”

Section: Performance Analysis and Testmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

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Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

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“…The Honey Badger Algorithm (HBA) is a bio-inspired technique that is developed based on the intelligent foraging behavior of honey badger. In the design of HBA, in addition to the search behavior of honey badgers, their honey-finding and digging strategies are also employed and modeled [35]. The Starling Murmuration Optimizer (SMO) is a bio-inspired algorithm that is formed based on the imitation of the starlings' behaviors during their stunning murmuration.…”

Section: Introductionmentioning

confidence: 99%

Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

Dehghani

Trojovský

2022

Sensors

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With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.

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Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

Cited by 574 publications

References 37 publications

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

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