# IHBOFS: A Biomimetics-Inspired Hybrid Breeding Optimization Algorithm for High-Dimensional Feature Selection

## TL;DR

This paper introduces IHBOFS, a new optimization algorithm inspired by biomimetics, which improves feature selection in high-dimensional data by combining adaptive strategies and enhancing exploration.

## Contribution

The novel contribution is the integration of adaptive strategies and mechanisms like Good Point Set and Elite Opposition-Based Learning in a hybrid optimization framework for feature selection.

## Key findings

- IHBOFS achieves an average classification accuracy of 92.57% on real-world datasets.
- The algorithm outperforms nine metaheuristic methods in high-dimensional feature selection tasks.
- Ablation studies on CEC2022 benchmarks confirm the effectiveness of the proposed strategies.

## Abstract

With the explosive growth of data across various fields, effective data preprocessing has become increasingly critical. Evolutionary and swarm intelligence algorithms have shown considerable potential in feature selection. However, their performance often deteriorates in large-scale problems, due to premature convergence and limited exploration ability. To address these limitations, this paper proposes an algorithm named IHBOFS, a biomimetics-inspired optimization framework that integrates multiple adaptive strategies to enhance performance and stability. The introduction of the Good Point Set and Elite Opposition-Based Learning mechanisms provides the population with a well-distributed and diverse initialization. Furthermore, adaptive exploitation–exploration balancing strategies are designed for each subpopulation, effectively mitigating premature convergence. Extensive ablation studies on the CEC2022 benchmark functions verify the effectiveness of these strategies. Considering the discrete nature of feature selection, IHBOFS is further extended with continuous-to-discrete mapping functions and applied to six real-world datasets. Comparative experiments against nine metaheuristic-based methods, including Harris Hawk Optimization (HHO) and Ant Colony Optimization (ACO), demonstrate that IHBOFS achieves an average classification accuracy of 92.57%, confirming its superiority and robustness in high-dimensional feature selection tasks.