Non-dominated Ranked Genetic Algorithm for Solving Multi-objective Optimization Problems: Nrga

Multi-objective evolutionary algorithms (EAs) that use non-dominated sorting and sharing have been criticized. Mainly for their: 1) ( 3 MN O computational complexity (where M is the number of objectives and N is the population size). 2Non-elitism approach; 3-the need for specifying a sharing parameter. In this paper, a method combining the new Ranked based Roulette Wheel selection algorithm with Pareto-based population ranking Algorithm is proposed, named Non-dominated Ranking Genetic Algorithm (NRGA), which alleviates most of the above three difficulties. A two tier ranked based roulette wheel selection operator is presented that creates a mating pool from the parents’ population by selecting the best (with respect to fitness and spread) solutions stochastically. Simulation results on benchmark test problems show that the proposed NRGA, in most of the problems, is able to find much better spread of solutions and faster convergence near the true Pareto-optimal front compared to NSGA-II other elitist MOEA that pay special attention to creating a diverse Pareto-optimal front. Much better performance of NRGA is observed.