https://doi.org/10.1140/epjp/s13360-023-04024-y
Regular Article
Grey wolf optimization and enhanced stochastic fractal search algorithm for exoplanet detection
1
Department of CSE, Cambridge Institute of Technology, Bangalore, India
2
Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India
3
Department of Computer Science and Engineering, University Centre for Research and Development, Chandigarh University, Mohali, Punjab, India
4
Department of Computer Science, King Khalid University, Muhayel, Aseer, Kingdom of Saudi Arabia
5
Department of Mechanical Engineering, Lebanese American University, Beirut, Lebanon
6
Department of Mathematics and Statistics, Riphah International University, I-14, 44000, Islamabad, Pakistan
7
Center of Research, Faculty of Engineering, Future University in Egypt, 11835, New Cairo, Egypt
f
scientificresearchglobe@gmail.com
Received:
24
December
2022
Accepted:
25
April
2023
Published online:
19
May
2023
Detection of Exoplanet had been an ‘intensely active’ exploration area within Astronomy where several attempts are made. In the proposed research work, exoplanet detection was done using a Kepler Dataset. Data pre-processing was carried out through Mean Imputation which was found to be the most common procedure of replacing missing value. For assessing Imputation Method’s performance, Normalized Root Mean Square Error was calculated. In feature selection method, a novel combination of Grey Wolf Optimizer (GWO) based on Enhanced Stochastic Fractal Search Algorithm (ESFSA) had been utilized, in a more advanced manner, for reducing the number of normalized input values to those which were highly beneficial. Lastly, after finding the best optimum values and delivering them to Random Forest (RF), the exoplanet got classified into 3 categories—False Positive, Not Detected as well as Candidate. The research work also showed the quantitative analysis of proposed GWO-based ESFSA with other feature selection methods and RF classifier with other existing classifiers. Overall comparative analysis of the proposed method with other related works (present in the literature) was also carried out. As observed, GWO-based ESFSA provided outstanding results—99.74% of recall, 99.80% of specificity, 99.81% of accuracy, 99.98% of sensitivity, 98.84% of precision and 97.21% of F1-score, and proved its superiority over existing methods.
The original online version of this article was revised: In this article the affiliation details for author B. R. Sreenivasa were incorrectly given as ‘School of CSE, REVA University, Bangalore, India’ but should have been ‘Department of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India’.
A correction to this article is available online at https://doi.org/10.1140/epjp/s13360-023-04255-z.
Copyright comment corrected publication 2023
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. corrected publication 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
