Gradient-based optimizer improved by Slime Mould Algorithm for global optimization and feature selection for diverse computation problems

Abstract

Optimization algorithms have shown significant advantages in solving diverse several real-world problems, especially where are limitations in computations and hardware requirements. However, they might get trapped in local optima or fast convergence. To solve these challenges, the exploration ability of an optimization algorithm should be evaluated and improved. This paper proposes a new approach for solving global optimization and feature selection problems to overcome these drawbacks and balance exploitation and exploration abilities. We propose a hybrid approach combining both, Gradient-Based Optimizer (GBO) using Slime Mould Algorithm (SMA), which exploits the strength factors of Gradient-Based Optimizer and Slime Mould Algorithm. The SMA is applied as a local search for the GBO to enhance its ability to explore more regions in the search space. This strategy is employed to achieve a balance between both exploration and exploitation. Two main experiments are applied to evaluate the performance of the GBOSMA. The first one solves a well-known global optimization problem (CEC2017), whereas the second one solves feature selection problems using several benchmark datasets. The GBOSMA is compared to the standard GBO, SMA, and the most recent optimization algorithms in both experiments. The results also show that the GBOSMA approach obtains promising results in both experiments outperforming other algorithms. Comparing with eight well-known optimization algorithms, the results (for both global optimization and feature selection problems) demonstrate that the GBOSMA overcomes others in terms of performance, speed and stability.