MATLAB Implementation of Differential Evolution Algorithm

In this article, we will discuss the Differential Evolution (DE) algorithm, which represents an efficient optimization technique particularly suitable for multi-objective optimization problems. The algorithm operates through three main phases: mutation, crossover, and selection. In MATLAB implementation, the mutation phase typically generates new candidate solutions by combining weighted differences between population members, while the crossover phase creates trial vectors by mixing mutant and target vectors. The selection phase then chooses better solutions for the next generation based on fitness comparison.

Beyond its core functionality, we will explore practical application domains of differential evolution, including industrial manufacturing optimization and artificial intelligence applications such as neural network training and parameter tuning. The algorithm's implementation in MATLAB typically involves key functions like population initialization using 'rand()', vector manipulation through matrix operations, and fitness evaluation using objective functions.

We will also analyze the advantages and disadvantages of differential evolution, highlighting its simple implementation structure, few control parameters, and strong global search capability. Comparative analysis with other optimization algorithms like Genetic Algorithms and Particle Swarm Optimization will demonstrate DE's distinctive characteristics. Ultimately, differential evolution presents a valuable research subject, and through this article we aim to help more researchers appreciate the algorithm's potential in solving complex optimization challenges.

Sample MATLAB code structure typically includes: population initialization with boundary constraints, main optimization loop with mutation strategies (like rand/1 or best/1), binomial crossover operations, and selection mechanisms using objective function evaluations. The algorithm's efficiency can be enhanced through parameter adaptation techniques for mutation factors and crossover rates.