SP-J48: a novel optimization and machine-learning-based approach for solving complex problems: special application in software engineering for detecting code smells

This paper presents a novel hybrid algorithm based on optimization and machine-learning approaches for solving real-life complex problems. The optimization algorithm is inspired from the searching and attacking behaviors of sandpipers, called as Sandpiper Optimization Algorithm ( SPOA ). These two b...

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Veröffentlicht in:Neural computing & applications 2020-06, Vol.32 (11), p.7009-7027
Hauptverfasser: Kaur, Amandeep, Jain, Sushma, Goel, Shivani
Format: Artikel
Sprache:eng
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Zusammenfassung:This paper presents a novel hybrid algorithm based on optimization and machine-learning approaches for solving real-life complex problems. The optimization algorithm is inspired from the searching and attacking behaviors of sandpipers, called as Sandpiper Optimization Algorithm ( SPOA ). These two behaviors are modeled and implemented computationally to emphasize intensification and diversification in the search space. A comparison of the proposed SPOA algorithm is performed with nine competing optimization algorithms over 23 benchmark test functions. The proposed SPOA is further hybridized with B-J48 pruned machine-learning approach for efficiently detecting the code smells from the data set. The results reveal that the proposed technique is able to solve challenging problems and outperforms the other well-known approaches.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04175-z