Instigating the Sailfish Optimization Algorithm Based on Opposition-Based Learning to Determine the Salient Features From a High-Dimensional Dataset

The aim of this research critique is to propose a hybrid combination of Opposition-Based Learning and Sailfish Optimization strategy to recognize the salient features from a high-dimensional dataset. The Sailfish Optimization is a swarm-based metaheuristics optimization algorithm inspired by the foraging strategy of a group of Sailfish. Sailfish Optimization explores the search space in only one direction, limiting its converging capacity and causing local minima stagnation. Convergence will be optimal if the search space is reconnoitred in both directions, improving classification accuracy. As a result, combining the Opposition-Based Learning and Sailfish Optimization strategies improves SFO's exploration capability by patrolling the search space in all directions. Sailfish Optimization Algorithm based on Opposition-Based Learning successfully amalgamates the model to global optima at a faster convergence rate and better classification accuracy. The recommended method is tested with six different cancer microarray datasets for two different classifiers: the Support Vector Machine classifier and the K-Nearest Neighbor classifier. From the results obtained, the proposed model aided with Support Vector Machine outperforms the existing Sailfish Optimization with or without K-Nearest Neighbor in terms of convergence capability, classification accuracy, and selection of the most delicate salient features from the dataset.

Automated summary

This research proposes a hybrid combination of Opposition-Based Learning and Sailfish Optimization strategy to recognize salient features in high-dimensional datasets. The method improves exploration capability and convergence rate, achieving better classification accuracy compared to existing methods.