Optimizing Complex Challenges: Harnessing the Power of Genetic Algorithms and the Nelder-Mead Simplex Algorithm for Effective Problem Solving

This paper introduces a hybrid optimization framework that integrates the genetic algorithm (GA) and the Nelder-Mead simplex Algorithm (NMA) to address optimization challenges across benchmark functions and real-world problems. By synergistically combining the exploration capabilities of the GA and the local refinement prowess of the NMA, our approach aims to provide a robust and versatile solution for a wide range of optimization scenarios. We demonstrate the effectiveness of the hybrid approach through extensive experimentation on 20 benchmark functions and two real-world optimization challenges, demonstrating its adaptability, efficiency, and potential to outperform standalone methods.