Mustafa Salam Kadhm, Mamoun Jassim Mohammed, Sufyan Othman Zaben
الملخص الانجليزي
Feature selection consider one of the essential pre-processing stage of the classification task in machine
learning. The datasets that used in classification contain irrelevant features that may directly affect the performance of
the used classifiers. The classification accuracy could be race using appropriate feature selector by reducing the number
of the extracted features from the datasets. The common and the powerful algorithms that successfully used for feature
selection task is the optimization algorithms. Based on the searching strategy of butterflies, the Butterfly Optimization
Algorithm (BOA) is a meta-heuristic swarm intelligence algorithm. Because of its performance, BOA has been applied
to a wide range of optimization problems. However, BOA has limitations such as reduced population variety and a
tendency to become locked in a local optimum. Besides, it suffers in converges speed, accuracy, and precision of the
optimal objective value when optimizing high dimensional problems. Therefore, this paper proposed an accurate
algorithm based on BOA and Sine-Cosine Algorithm called BOA-SC. The BOA first improved via the update
equations then hybrid with SC to enhance the local search stage for better optimization results. Using the improvement
strategy and SC enhance the performance of BOA and solve the lower coverage and local optima issues that BOA
suffers from. The performance of the proposed hybrid algorithm is evaluated using two assessments via converges
speed, the accuracy, and precision of the optimal objective value. First, 23 benchmark functions used to evaluate
proposed algorithm that achieved a high optimization result comparing with six most recent metaheuristic algorithms
puzzle optimization algorithm (POA), northern goshawk optimization (NGO), coati optimization algorithm (COA),
swarm bipolar algorithm (SBA), apiary organizational-based optimization algorithm (AOOA), and swarm space
hopping algorithm (SSHA). The obtained results show that, BOA-SC is better than POA, NGO, COA, SBA, AOOA,
and SSHA, in 5, 6, 8, 13, 18, 22, and 23 functions. In the second evaluation, the proposed algorithm compared with
four BOA variants algorithms s-shaped binary butterfly optimization algorithm (S-bBOA), dynamic butterfly
optimization algorithm (DBOA), chaotic butterfly optimization algorithm (CBOA), and optimization and extension of
binary butterfly optimization approaches (OEbBOA) which are employed for feature selections methods. The results
of BOA-SC are funnier than S-bBOA, DBOA, CBOA, and OEbBOA in three distinct datasets (Sonar, Waveform, and
Spect) by archiving a high classification accuracy 97%, 86%, and 87% as a feature selection algorithm for the
classification task.
أ.م.د مصطفى سلام كاظم | Mustafa Salam Kadhm | 15762