MATLAB Implementation of Differential Evolution Algorithm

The Differential Evolution program is an optimization algorithm that simulates evolutionary processes to find optimal solutions for functions. The algorithm implementation includes detailed code comments, enabling users to directly modify and execute functions for their specific optimization needs. Differential Evolution is a population-based global optimization algorithm that operates through three main evolutionary operators: mutation, crossover, and selection. The algorithm maintains a population of candidate solutions and iteratively improves them by generating new trial vectors through differential mutation (typically using differences between randomly selected population members), performing crossover operations to combine parent and trial vectors, and applying selection mechanisms to retain better solutions. Key implementation aspects include: - Population initialization with random vectors within specified bounds - Mutation strategies (e.g., DE/rand/1, DE/best/1) that create donor vectors - Binomial or exponential crossover operations to generate trial vectors - Greedy selection between original and trial vectors based on fitness evaluation - Parameter controls for mutation factor (F), crossover rate (CR), and population size Due to its simplicity and efficiency, Differential Evolution finds widespread applications in various domains including neural network training (optimizing weights and biases), image processing (parameter optimization for filters), machine learning (hyperparameter tuning), and engineering design optimization. The MATLAB implementation provides a flexible framework where users can easily adapt the objective function, adjust algorithm parameters, and modify evolutionary operators to suit specific problem requirements.