NSGA-II is a multiobjective genetic algorithm that addresses the computational complexity, nonelitism, and sharing parameter issues of existing algorithms. It uses a fast nondominated sorting approach with O(MN²) complexity and an elitist selection operator to maintain diversity and convergence. Simulation results show NSGA-II outperforms PAES and SPEA in finding diverse solutions and converging near the true Pareto-optimal front. It also handles constrained problems efficiently by modifying the dominance definition. NSGA-II is compared with other algorithms on various test problems, demonstrating better performance in convergence and diversity. The algorithm is also tested on rotated problems with epistatic interactions, showing its effectiveness in handling complex multiobjective scenarios. The constrained NSGA-II uses a binary tournament selection method to handle constraints without penalty parameters, maintaining computational efficiency. Overall, NSGA-II provides a robust solution for multiobjective optimization problems.NSGA-II is a multiobjective genetic algorithm that addresses the computational complexity, nonelitism, and sharing parameter issues of existing algorithms. It uses a fast nondominated sorting approach with O(MN²) complexity and an elitist selection operator to maintain diversity and convergence. Simulation results show NSGA-II outperforms PAES and SPEA in finding diverse solutions and converging near the true Pareto-optimal front. It also handles constrained problems efficiently by modifying the dominance definition. NSGA-II is compared with other algorithms on various test problems, demonstrating better performance in convergence and diversity. The algorithm is also tested on rotated problems with epistatic interactions, showing its effectiveness in handling complex multiobjective scenarios. The constrained NSGA-II uses a binary tournament selection method to handle constraints without penalty parameters, maintaining computational efficiency. Overall, NSGA-II provides a robust solution for multiobjective optimization problems.