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:	Staff Zu Site Abstract In Staff Site
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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