NSGA-II Multi-Objective Optimization Algorithm Implementation for ZDT2 Test Function

In this article, we demonstrate the implementation of the NSGA-II algorithm for optimizing the ZDT2 benchmark function. As a standard test problem in multi-objective optimization, the ZDT2 function features multiple Pareto-optimal solutions, making it ideal for evaluating algorithm performance. While challenging for beginners, we systematically break down each algorithmic component with practical code implementations. Our implementation includes key NSGA-II operations: non-dominated sorting for solution classification, crowding distance calculation for diversity maintenance, and binary tournament selection for parent generation. The code structure demonstrates population initialization, fitness evaluation, and the evolutionary loop with crossover and mutation operations. Through this detailed walkthrough, beginners can comprehend the algorithm's mechanism and apply these concepts to other optimization problems. The article serves as a comprehensive guide for algorithm practitioners starting their journey in multi-objective optimization using NSGA-II.