On the Convergence of NSGA II Algorithm to a Single Pareto Optimal Solution with a Continuous and Quadratic Multi objective Optimization Program
In this paper, we test a fast elitist Non Dominant Sorting Genetic Algorithm (NSGA II) on a continuous quadratic multiobjective optimization problem. We use a big size of multiobjective programming for the Gross Domestic Product (GDP) quarterly disaggregation and we test different values of the NSGA II parameters. We come to the conclusion that if the parameters are judiciously chosen, the NSGA II algorithm converges to a single Pareto optimal solution in a regular and bounded set for the quadratic multiobjective optimization problem we used. It should be noted that elitism in the NSGA II algorithm contributes to accelerating the rate of convergence and the overall performance of the genetic algorithm incorporated in it.