MATLAB Implementation of Basic Differential Evolution Algorithm

This documentation presents the MATLAB implementation of the basic differential evolution algorithm (Version A). We begin by reviewing the fundamental concepts of differential evolution, including its mutation strategy (typically DE/rand/1), crossover operations, and selection mechanisms for solving optimization problems. The implementation utilizes MATLAB's vectorization capabilities for efficient population management, where each individual represents a potential solution vector. The algorithm workflow includes initialization of population parameters, differential mutation using donor vectors, binomial crossover for offspring generation, and greedy selection between parent and trial vectors. All code examples have been rigorously debugged in MATLAB 7.0, featuring detailed comments on key functions such as population initialization, fitness evaluation, and convergence monitoring. Through this documentation, you will learn practical implementation techniques including parameter tuning for scaling factors and crossover rates, boundary constraint handling methods, and performance optimization strategies for computational efficiency.