Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Multi-objective Optimisation Based on Relation Favour
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
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
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
Optimization of scalarizing functions through evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Robust multi-objective optimization in high dimensional spaces
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
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
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A number of multiobjective evolutionary algorithms, together with numerous performance measures, have been proposed during past decades. One measure that has been popular recently is the hypervolume measure, which has several theoretical advantages. However, the well-known nondominated sorting genetic algorithm II (NSGA-II) shows a fluctuation or even decline in terms of hypervolume values when applied to many problems. We call it the “hypervolume degeneration problem”. In this paper we illustrated the relationship between this problem and the crowding distance selection of NSGA-II, and proposed two methods to solve the problem accordingly. We comprehensively evaluated the new algorithm on four well-known benchmark functions. Empirical results showed that our approach is able to alleviate the hypervolume degeneration problem and also obtain better final solutions.