NSGA-II Algorithm Implementation in MATLAB

In this article, we provide a comprehensive discussion of the MATLAB implementation for the NSGA-II algorithm. We begin with an overview of the algorithm's background and fundamental principles, including its non-dominated sorting approach, crowding distance calculation, and elitism preservation mechanism. The implementation details cover key MATLAB functions such as population initialization, tournament selection, simulated binary crossover (SBX), and polynomial mutation operations. We explain each step of the code with clear annotations, demonstrating how to handle multiple objectives and constraints through appropriate fitness function design. Furthermore, we explore potential applications and extensions of the algorithm, along with guidance on modifying and customizing the code for specific research needs. This article serves as a complete and practical resource for researchers and practitioners interested in multi-objective optimization using NSGA-II and MATLAB programming.