A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection

Elaziz, Mohamed E. Abd; Ewees, Ahmed A.; Oliva, Diego; Duan, Pengfei; Xiong, Shengwu

doi:10.1007/978-3-319-70139-4_15

Cited by 52 publications

(19 citation statements)

References 22 publications

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“…These algorithms are used in this comparison since they established their performance in different applications such as global optimization and feature selection methods [35][36][37][38][39]. The quality of each FS algorithm assessed by using three measures: Accuracy, the ratio of the selected features, and the fitness value where the accuracy (Acc) defined as:…”

Section: Evaluation Of the Proposed Modelmentioning

confidence: 99%

New machine learning method for image-based diagnosis of COVID-19

et al. 2020

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COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new MLmethod proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

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Section: Evaluation Of the Proposed Modelmentioning

confidence: 99%

New machine learning method for image-based diagnosis of COVID-19

et al. 2020

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“…The SCA has a small number of parameters and also its ability to find the optimal solution is better than other metaheuristic (MH) algorithms; therefore, the SCA has been used in many fields. For examples, Elaziz et al, in [34] applied SCA to solve features selection problem; whereas, in [35] SCA was used to select the relevant features to enhance the performance of classification the galaxy images. The authors of [36] efficiently applied SCA to train the feed-forward neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

et al. 2019

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This paper presents a hybrid method of Simulated Annealing (SA) algorithm and Sine Cosine Algorithm (SCA) to solve unrelated parallel machine scheduling problems (UPMSPs) with sequence-dependent and machine-dependent setup times. The proposed method, called SASCA, aims to improve the SA algorithm using the SCA as a local search method. The SCA provides a good tool for the SA to avoid getting stuck in a focal point and improving the convergence to an efficient solution. SASCA algorithm is used to solve UPMSPs by minimizing makespan. To evaluate the performance of SASCA, a set of experiments were performed using 30 tests for 4 problems. Moreover, the performance of the proposed method was compared with other meta-heuristic algorithms. The comparison results showed the superiority of SASCA over other methods in terms of performance dimensions.

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“…This hybrid approach utilized the Differential Evolution (DE) operators as local search method to improve the SCA. The experimental results of the proposed approach were compared with three well-known algorithms (SCA, SSO, and ABC) and they proved to be highly noteworthy [22].…”

Section: Literature Reviewsmentioning

confidence: 99%

A Hybrid SCA Inspired BBO for Feature Selection Problems

Sindhu

Ngadiran

Yacob

et al. 2019

Mathematical Problems in Engineering

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Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

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A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection

Cited by 52 publications

References 22 publications

New machine learning method for image-based diagnosis of COVID-19

New machine learning method for image-based diagnosis of COVID-19

Sine-Cosine Algorithm to Enhance Simulated Annealing for Unrelated Parallel Machine Scheduling with Setup Times

A Hybrid SCA Inspired BBO for Feature Selection Problems

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