An Improved Evolutionary Algorithm for Multi-objective Optimization

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for the problems, (1) O(mN³) computational complexity (where m is the number of objectives and n is the population size), (2) non-elitism approach, and (3) the need for specifying a sharing parameter. This paper suggests a non-dominated sorting based the multi-objective evolutionary algorithm INSGA which alleviates all the above three difficulties. Simulation results on five difficult test problems show that the proposed INSGA is able to find much better spread of solutions in all problems compared to NSGA.