Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Fuzzy-Pareto-Dominance and its application in evolutionary multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An improved secondary ranking for many objective optimization problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constrained many-objective optimization: a way forward
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ranking Methods for Many-Objective Optimization
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A hybrid scalarization and adaptive ε-ranking strategy for many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Effects of the existence of highly correlated objectives on the behavior of MOEA/D
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Alleviate the hypervolume degeneration problem of NSGA-II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
Hi-index | 0.00 |
Many-objective optimization refers to optimization problems with a number of objectives considerably larger than two or three. In this paper, a study on the performance of the Fast Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for handling such many-objective optimization problems is presented. In its basic form, the algorithm is not well suited for the handling of a larger number of objectives. The main reason for this is the decreasing probability of having Pareto-dominated solutions in the initial external population. To overcome this problem, substitute distance assignment schemes are proposed that can replace the crowding distance assignment, which is normally used in NSGA-II. These distances are based on measurement procedures for the highest degree, to which a solution is nearly Pareto-dominated by any other solution: like the number of smaller objectives, the magnitude of all smaller or larger objectives, or a multi-criterion derived from the former ones. For a number of many-objective test problems, all proposed substitute distance assignments resulted into a strongly improved performance of the NSGA-II.