A micro multi-objective genetic algorithm for multi-objective optimizations

A micro multi-objective genetic algorithm based on the micro genetic algorithm is suggested for the multi-objective 14 optimization problems. An external elite archive is used to store Pareto-optimal solutions of the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. A crowded-comparison approach is used in each level to keep the diversity of the population. All solutions from the first non-dominated level make up of the current non-dominated set. Once the small population converges, an exploratory operator will be applied to the external elite population to explore more non-dominated solutions near the current non-dominated set, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGA II.