An Improved Elitist Strategy Multi-Objective Evolutionary Algorithm

NSGA II (fast elitist non-dominated sorting genetic algorithm) is one of better elitist multi-objective evolutionary algorithm. It doesn´t limit the elitist extent, which will result in prematurely converging to local Pareto-optimal front. To avoid prematurely convergence, diversity of individuals should be kept in search process. In this paper, an improved elitist strategy multi-objective evolutionary algorithm is proposed, it uses a distribution function to control elitist and to get better diversity of individuals, the extent of elitist can be changed by fixing a user-defined parameter. A performance metric is used for evaluating diversity. Simulation results on four difficult test problems show that the proposed algorithm is able to find much better spread of solutions and better convergence near the true Pareto-optimal front than NSGA II