Mantis Search Algorithm Integrated with Opposition-Based Learning and Simulated Annealing for Feature Selection

Faculty Computer Science Year: 2025
Type of Publication: ZU Hosted Pages:
Authors:
Journal: Sustainable Machine Intelligence Journal Sciences Force Volume:
Keywords : Mantis Search Algorithm Integrated with Opposition-Based    
Abstract:
the Mantis Search Optimization algorithm is combined with opposition based learning and Simulated Annealing algorithms to solve the feature selection problem. The proposed algorithm was applied to 21 datasets characterized by a large number of instances, a large dimensionality (number of features), or both, to test the efficiency and performance of the proposed algorithm. Several performance factors were conducted to evaluate the results of comparing the proposed OBMSASA algorithm with other existing algorithms.
    
     
 
       

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