Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Many-Objective Particle Swarm Optimization by Gradual Leader Selection
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Heatmap visualization of population based multi objective algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Substitute distance assignments in NSGA-II for handling many-objective optimization problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th 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 multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Research advances in automated red teaming
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
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
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Non-dominated Sorting Genetic Algorithm (NSGA-II) [1] and the Strength Pareto Evolutionary Algorithm (SPEA2) [2] are the two most widely used evolutionary multi-objective optimization algorithms. Although, they have been quite successful so far in solving a wide variety of real life optimization problems mostly 2 or 3 objective in nature, their performance is known to deteriorate significantly with an increasing number of objectives. The term many objective optimization refers to problems with number of objectives significantly larger than two or three. In this paper, we provide an overview of the challenges involved in solving many objective optimization problems and provide an in depth study on the performance of recently proposed substitute distance based approaches, viz. Subvector dominance, -eps-dominance, Fuzzy Pareto Dominance and Sub-objective dominance count for NSGA-II to deal with many objective optimization problems. The present study has been conducted on scalable benchmark functions (DTLZ2-DTLZ3) and the recently proposed P* problem [3] since their convergence and diversity measures can be compared conveniently. An alternative substitute distance approach is introduced in this paper and compared with existing ones on the set of benchmark problems.