Implementation of Multi-Objective Genetic Algorithm NSGA-II with Code Annotation

This article presents the implementation of the NSGA-II multi-objective genetic algorithm with comprehensive code annotations and relevant research papers. The algorithm employs key techniques including fast non-dominated sorting, crowding distance calculation, and elitist selection to maintain population diversity while converging to Pareto-optimal solutions. It can be applied to various domains such as optimization design, resource allocation, and queuing systems. The implementation includes well-documented functions for initialization, genetic operations (crossover and mutation), and solution evaluation. We believe this algorithm is highly practical and users can easily customize it for their specific problems through minor adjustments to the objective functions and constraints. Additionally, we provide example code with implementation notes to help readers better understand and apply the algorithm effectively. We hope this resource proves valuable and offers practical insights for real-world applications.