An efficient hybrid simplex bat algorithm (HSBA) for solving the general nonlinear global optimization problem

In this paper, we propose an efficient hybrid meta-heuristic algorithm combining the Nelder–Mead (NM) simplex search method with the Bat Algorithm (BA), termed as the Hybrid Simplex-Bat Algorithm (HSBA). The proposed method aims to leverage the local search strength of NM and the global exploration ability of BA to solve complex nonlinear global optimization problems. HSBA is tested on a suite of standard benchmark functions and compared with existing meta-heuristic and hybrid algorithms. Experimental results demonstrate that HSBA outperforms the standalone BA and many other existing hybrid approaches in terms of solution quality, convergence speed, and robustness.