S-shaped Binary Whale Optimization Algorithm for Feature Selection

Hussien, Abdelazim G.; Hassanien, Aboul Ella; Houssein, Essam H.; Bhattacharyya, Siddhartha; Amin, Mohamed

doi:10.1007/978-981-10-8863-6_9

Cited by 151 publications

(81 citation statements)

References 13 publications

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“…Feature selection (FS) can be defined as the process of selecting the most critical dataset and removing irrelevant ones [143], [144]. If it is a wrapper method, it is often related to the neural networks in its application part [189], [190].…”

Section: ) Feature Selectionmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: ) Feature Selectionmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

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“…In fact, the methodology of hybridizing MAs is widely used in different domains of optimization other than feature selection 44 . In this vein, combinations of different ML techniques and MAs (e.g., search strategies) have been applied in many fields with modifications and hybridization to benefit from one technique in uplifting search efficiency.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

Houssein

Hosney²,

Elhoseny

et al. 2020

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one of the major drawbacks of cheminformatics is a large amount of information present in the datasets. In the majority of cases, this information contains redundant instances that affect the analysis of similarity measurements with respect to drug design and discovery. therefore, using classical methods such as the protein bank database and quantum mechanical calculations are insufficient owing to the dimensionality of search spaces. In this paper, we introduce a hybrid metaheuristic algorithm called cHHo-cS, which combines Harris hawks optimizer (HHo) with two operators: cuckoo search (cS) and chaotic maps. the role of cS is to control the main position vectors of the HHo algorithm to maintain the balance between exploitation and exploration phases, while the chaotic maps are used to update the control energy parameters to avoid falling into local optimum and premature convergence. feature selection (fS) is a tool that permits to reduce the dimensionality of the dataset by removing redundant and non desired information, then fS is very helpful in cheminformatics. FS methods employ a classifier that permits to identify the best subset of features. the support vector machines (SVMs) are then used by the proposed cHHo-cS as an objective function for the classification process in FS. The CHHO-CS-SVM is tested in the selection of appropriate chemical descriptors and compound activities. Various datasets are used to validate the efficiency of the proposed CHHO-CS-SVM approach including ten from the UCI machine learning repository. Additionally, two chemical datasets (i.e., quantitative structure-activity relation biodegradation and monoamine oxidase) were utilized for selecting the most significant chemical descriptors and chemical compounds activities. the extensive experimental and statistical analyses exhibit that the suggested CHHO-CS method accomplished much-preferred trade-off solutions over the competitor algorithms including the HHO, CS, particle swarm optimization, moth-flame optimization, grey wolf optimizer, Salp swarm algorithm, and sine-cosine algorithm surfaced in the literature. the experimental results proved that the complexity associated with cheminformatics can be handled using chaotic maps and hybridizing the meta-heuristic methods. The prediction and analysis of molecules are essential tasks in cheminformatics, which use methods from mathematics and computer science to enhance their performance. The implementation of these methods depends on databases. The processes that generate most of the affectations are the storage and retrieval of molecular structures and properties (e.g., pharmacogenomics data). Typically, the behavior of the compounds can be investigated using molecular analysis. The molecular analysis helps to develop and test molecules for decreasing the effects of specific diseases 1. One drawback associated with cheminformatics is the exponential increment of the search space owing to features in the dataset 2. However, cheminformatics is still being widely used in drug design, where ...

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“…Slime mould algorithm (SMA) [46]. Due to its efficiency and effectiveness, these MEAs have been used in many problems, such as image segmentation in medical diagnosis, engineering design [47], feature selection [48], [49], workflow scheduling, and biopharmaceutical industry [39], [50]- [52]. As the latest member of MEAs, HHO performs a simple mechanism and can be used easily.…”

Section: Proposed Sglhhomentioning

confidence: 99%

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

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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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S-shaped Binary Whale Optimization Algorithm for Feature Selection

Cited by 151 publications

References 13 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Hybrid Harris hawks optimization with cuckoo search for drug design and discovery in chemoinformatics

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

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