A fast and elitist multiobjective genetic algorithm: NSGA-II

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( 3) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominated sorting genetic algorithm II (NSGA-II), which alleviates all the above three difficulties. Specifically, a fast nondominated sorting approach with ( 2) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best (with respect to fitness and spread) solutions. Simulation results on difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to Pareto-...

• Type: Journal
• Year: 2002
• Where: TEC
• Authors: Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T. Meyarivan
• Keywords: Computational Complexity, Multiobjective Evolutionary Algorithms, Nondominated Sorting, TEC 2002