MATLAB Implementation of Differential Evolution Algorithm with Code Examples

This article demonstrates how to program differential evolution algorithms using MATLAB. We will present the fundamental algorithm framework including complete code implementations for key operations such as mutation and competitive selection. The implementation covers population initialization through MATLAB's matrix operations, mutation strategies using vectorized computations for efficiency, and crossover operations with logical indexing. We will explain critical functions like rand() for random number generation, logical indexing for selection processes, and vectorized operations for fast fitness evaluation. Practical optimization techniques include parameter tuning strategies for scaling factors and crossover rates, along with performance benchmarking methods using tic/toc timing functions. Detailed explanations will help you understand algorithm implementation nuances, from basic MATLAB syntax for beginners to advanced vectorization techniques for experienced users. The content is structured to provide comprehensive guidance for implementing and optimizing differential evolution algorithms in MATLAB environments.