NSGA-II Multi-Objective Optimization MATLAB Code Implementation

This article presents a comprehensive guide to solving complex optimization problems using the NSGA-II multi-objective optimization algorithm. NSGA-II is a genetic algorithm-based optimization technique that efficiently explores the solution space when handling multiple objective functions. We provide a complete MATLAB implementation of NSGA-II, including key functions for non-dominated sorting, crowding distance calculation, and selection operations. The code structure demonstrates how to initialize populations, perform binary tournament selection, implement simulated crossover and mutation operators, and manage elitism through combined parent-offspring populations. Additionally, we explain the fundamental working principles of NSGA-II and provide guidance on parameter tuning strategies for population size, crossover rate, mutation probability, and maximum generations to achieve optimal results. Finally, we present practical application cases of NSGA-II in engineering design and resource allocation problems, showcasing how the algorithm performs in real-world scenarios with competing objectives.