Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization

This text provides comprehensive technical details about the implementation. The Non-dominated Sorting Genetic Algorithm (NSGA) represents an efficient evolutionary approach for addressing multi-objective optimization challenges, utilizing genetic operators including tournament selection, simulated binary crossover, and polynomial mutation. The MATLAB routine demonstrates a complete NSGA-II implementation featuring fast non-dominated sorting, crowding distance calculation for diversity maintenance, and elitism preservation. Key functions include population initialization with constraint handling, objective function evaluation, and Pareto-optimal solution extraction. This exemplary codebase allows users to modify genetic parameters (population size, crossover rate), incorporate custom objective functions, and adapt constraint handling mechanisms. Through this implementation, users gain practical insights into NSGA's working principles, including dominance relationships, front assignment, and diversity preservation strategies, enabling effective application to real-world optimization scenarios. Consequently, this routine serves as both an educational tool for understanding multi-objective evolutionary algorithms and a practical framework for solving complex engineering optimization problems.