Implementation and Validation of NSGA-II Algorithm for Constrained and Unconstrained Multi-Objective Optimization Problem
Adithya B Uday, Nivedita Naik, G. M. Madhu, C. Vyjayanthi, Chirag Modi · 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) · 2022
Multi-Objective optimization (MOO) algorithms are gaining more attention among designers or decision makers working on optimization problems in practice, compared to the single objective optimization algorithms that are limited to single objectives. Evolutionary Computation (EC) techniques are employed for solving these MOO problems. Techniques such as Non-dominated Sorting Genetic Algorithm (NSGA-I), its improved version called NSGA-II, Strength-Pareto Evolutionary Algorithm (SPEA), Multi-Objective Particle Swarm Optimization (MOPSO) are some widely employed EC techniques. The NSGA-II algorithm implementation and validation is carried out in this paper. The mathematical analysis of the NSGA-II algorithm and its implementation in the MATLAB environment are presented here. Developed algorithm is also tested using unconstrained and constrained standard test functions for MOO problems. The simulation results revealed that the developed algorithm is providing results similar to the standard test results. Hence, incorporating a suitable mathematical model of any practical MOO problem into the implemented algorithm will yield optimal solution for the multiple objectives under consideration.