Open Access

Aamir Rahim Baloch

• The development of the Automated Driving System (ADS) had a substantial significant impression on scientists. Throughout ADS, different units and controllers are installed, monitored, and analysed. The research is based on the theory of the Model Predictive Controller (MPC) system. which tackles the Multi-Objective Optimization (MOO) problem by calling out the cost function on the path-following problem using the MPC (i:e Model Predictive Path Following Control (MPFC)) with the defined constraints for the lateral control of the vehicle. The cost function helps to minimize the errors and compute the data. But the computation through the cost function is a bit complex. To overcome the complexity, an Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is proposed, which helps to minimize the errors of set parameters and provides an optimal solution. The notion of NSGA-II is to find a wide variety of solutions and obtain better convergence in the form of the Pareto Optimal Front. Moreover, the detail of the Non-Dominated Genetic Algorithm II is theoretically enlightened to understand the Evolutionary Algorithm (EA) concept and the workability of the genetic algorithm. And the whole function is scripted in the MATLAB program. Furthermore, we analyse the generated data in the Pareto Optimal Front and compare it with the Hyper-volume indicator, the most popular set-quality metric for rating stochastic multi-objective optimizer performance. Its theoretical characteristics, particularly the strict monotonicity concerning set dominance, which is still exclusive to hyper-volume based indicators, justify its widespread acceptance. Hyper-volume indicator scrutinizes the optimal solution of multiple results. The analysis and evaluation are completed between two other data sets generated and received through the RandomDATA and Multiple Objective Particle Swarm Optimization (MOPSO) algorithm, which describe the quality of the best optimal solution between the results generated by the three algorithms. Keywords: Non-Dominated Genetic Algorithm II, Model Predictive Path following Control, MATLAB, Algorithm, Pareto Optimal Front, Multi-Object optimization, Cost Function