Differential evolution (DE) is a population-based optimization algorithm that aims to find the global minimum of a function by exploring the solution space. The process involves generating a population of candidate solutions and iteratively improving them through mutation, crossover, and selection. The algorithm operates in a multi-dimensional space, where it evaluates the function across multiple independent and dependent variables to identify optimal solutions.For those struggling with the theoretical aspects, it is suggested to clarify specific points from Storn and Price's explanation, as this could lead to a better understanding. Additionally, practical examples or simplified walkthroughs of DE implementations are sought after, as many existing resources may be too complex for beginners. The transition from theoretical concepts to practical coding in FORTRAN is a common challenge, highlighting the need for more accessible examples tailored for those new to the algorithm.
