NSGA-II: A Classic Algorithm for Multi-objective Optimization

In the field of computer science, NSGA-II stands as a fundamental algorithm for addressing multi-objective optimization problems. Building upon genetic algorithm principles, it employs non-dominated sorting and crowding distance calculations to maintain population diversity while preserving elite individuals through selection, crossover, and mutation operations. The MATLAB implementation provided includes key functions such as non-dominated sorting for Pareto front identification, crowding distance computation for diversity maintenance, and tournament selection for parent selection. These algorithmic components work together to efficiently converge toward optimal Pareto fronts. For researchers and developers interested in NSGA-II, this repository offers complete MATLAB source code that demonstrates practical implementation details, including population initialization, fitness evaluation, and generational evolution processes. This comprehensive codebase enables deeper understanding of the algorithm's mechanics and facilitates integration into custom optimization projects.