Differential Evolution Algorithm (DE) and Particle Swarm Optimization (PSO) MATLAB Implementations

In this paper, I provide a detailed introduction to Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms, accompanied by complete MATLAB code implementations. Differential Evolution is a population-based stochastic optimization algorithm that searches for optimal solutions by simulating differences between individuals through mutation, crossover, and selection operations. The MATLAB implementation typically includes parameter initialization, population generation, mutation strategies (like rand/1 or best/1), binomial crossover, and selection mechanisms. Particle Swarm Optimization mimics the collective behavior of bird flocks or fish schools, where particles adjust their positions based on individual experience and social learning. The MATLAB code for PSO generally involves velocity updates using inertia weights, personal best tracking, global best identification, and position updates with boundary handling. Through this paper, you will understand the fundamental principles and practical applications of both algorithms, along with hands-on MATLAB implementation techniques including key functions like deoptim() and pso() with proper parameter tuning. I hope this resource proves valuable for your optimization projects!