Many-Objective Evolutionary Algorithms
University of Science and Technology of China · University of Birmingham
Abstract
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in…
Citation impact
- FWCI
- 70.86
- Percentile
- 100%
- References
- 223
Authors
4Topics & keywords
- Computer science
- Evolutionary algorithm
- Scalability
- Curse of dimensionality
- Pareto principle
- Dominance (genetics)
- Multi-objective optimization
- Mathematical optimization
Funding
- ECEuropean CommissionAward: 247619
- NNNational Natural Science Foundation of ChinaAwards: Grants 61175065 and 61329302, 61329302, 61175065, 247619
- UOUniversity of Science and Technology of China
- EAEngineering and Physical Sciences Research CouncilAwards: EP/J017515, Grant EP/J017515/1, EP/J017515/1, EP/J017515/1
- PFProgram for New Century Excellent Talents in UniversityAward: NCET-12-0512
- NKNational Key Research and Development Program of China