Enhanced Differential Evolution Algorithm for Global Optimization and Parameter Tuning

Abstract

The proposed research seeks to address these limitations by proposing an Enhanced Differential Evolution (EDE) algorithm to global optimization and parameter tuning by adding adaptive optimization mechanisms that enhance the performance of the search in terms of efficiency and convergence, and also the precision of the solution. The suggested method optimizes the traditional Differential Evolution (DE) framework with adaptive mutation schemes, parameter modification, and better population diversity management. These changes allow the algorithm to balance exploration of the global search space and exploitation of promising solutions better. The improved algorithm is tested on benchmark optimization functions and parameter optimization problems with nonlinear searches of complex search spaces. Some of the evaluation criteria used to measure performance are accuracy in optimization, rate of convergence, computing performance, stability of a solution, and robustness. The suggested method can be applied in machine learning optimization models, engineering design, scheduling, feature selection, and intelligent decision-support systems with great potential. This study demonstrates the usefulness of adaptive differential evolution methods to create efficient, accurate and scalable optimization algorithms.