Differential Evolution

Differential evolution (DE) is a population-based metaheuristic optimization algorithm used to find near-optimal solutions for complex problems where gradient information is unavailable. Current research focuses on enhancing DE's performance through adaptive parameter control, hybridization with other algorithms (e.g., genetic algorithms, gradient-based methods), and the development of novel mutation and crossover strategies tailored to specific problem types (e.g., constrained optimization, high-dimensional spaces, mixed-integer problems). These advancements are driving improvements in diverse fields, including robotics, computational neuroscience, and machine learning, by enabling efficient optimization of complex models and systems.