# MESPBO: Multi-Strategy-Enhanced Student Psychology-Based Optimization Algorithm for Global Optimization Problems and Feature Selection Problems

## TL;DR

This paper introduces MESPBO, an improved optimization algorithm that excels in solving global optimization and feature selection problems with better accuracy and efficiency.

## Contribution

The novel contribution is the integration of three strategies to enhance SPBO, achieving superior performance in optimization and feature selection tasks.

## Key findings

- MESPBO outperforms 11 metaheuristic algorithms on CEC2017 benchmarks across multiple dimensions.
- MESPBO achieves 100% classification accuracy on several datasets while selecting minimal feature subsets.
- Compared to SPBO, MESPBO improves fitness values by an average of 10% on seven datasets.

## Abstract

Feature selection and continuous optimization are fundamental yet challenging tasks in machine learning and engineering design. To address premature convergence and insufficient population diversity in Student Psychology-Based Optimization (SPBO), this paper proposes a Multi-Strategy-Enhanced Student Psychology-Based Optimizer (MESPBO). The proposed method incorporates three complementary strategies: (i) a hybrid heuristic initialization scheme based on Latin Hypercube Sampling and Gaussian perturbation; (ii) an adaptive dual-learning position update mechanism to dynamically balance exploration and exploitation; (iii) a hybrid opposition-based reflective boundary control strategy to enhance search stability. Extensive experiments on the CEC2017 benchmark suite with 10, 30, and 50 dimensions demonstrate that MESPBO consistently outperforms 11 state-of-the-art metaheuristic algorithms. Specifically, MESPBO achieves the best Friedman mean ranks of 2.00, 1.67, and 1.67 under 10D, 30D, and 50D settings, respectively, indicating superior convergence accuracy, robustness, and scalability. In real-world feature selection tasks conducted on 10 benchmark datasets, MESPBO achieves the highest average classification accuracy on 9 datasets, reaching 100% accuracy on several datasets, while maintaining competitive performance on the remaining one. Moreover, MESPBO selects the smallest feature subsets on 7 datasets, typically retaining only 2–4 features without sacrificing classification accuracy. Compared with the original SPBO, MESPBO further reduces the fitness values on 7 out of 10 datasets, achieving an average improvement of approximately 10%. These results verify that MESPBO provides an effective trade-off between optimization accuracy and feature compactness, demonstrating strong adaptability and generalization capability for both global optimization and feature selection problems.