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 |
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Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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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. |
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ISSN: | 0941-0643 1433-3058 |
DOI: | 10.1007/s00521-019-04175-z |