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Multiphase Balance of Diversity and Convergence in Multiobjective Optimization.
- Source :
- IEEE Transactions on Evolutionary Computation; Jun2019, Vol. 23 Issue 3, p503-513, 11p
- Publication Year :
- 2019
-
Abstract
- In multiobjective optimization, defining a good solution is a multifactored process. Most existing evolutionary multi- or many-objective optimization (EMO) algorithms have utilized two factors: 1) domination and 2) crowding levels of each solution. Although these two coarse-grained factors are found to be adequate in many EMO algorithms, their relative importance in an algorithm has been a matter of great concern to many current studies. We argue that beside these issues, other more fine-grained factors are of importance. For example, since extreme objective-wise solutions are important in establishing a noise-free and stable normalization process, reaching extreme solutions is more crucial than finding other solutions. In this paper, we propose an integrated algorithm, B-NSGA-III, that produces much better convergence and diversity preservation. For this purpose, in addition to emphasizing extreme objective-wise solutions, B-NSGA-III tries to find solutions near intermediate undiscovered regions of the front. B-NSGA-III addresses critical algorithmic issues of convergence and diversity-preservation directly through recent progresses in literature and integrates all these critical fine-grained factors seamlessly in an alternating phases scheme. The proposed algorithm is shown to perform better than a number of commonly used existing methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
TARDINESS
Subjects
Details
- Language :
- English
- ISSN :
- 1089778X
- Volume :
- 23
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Evolutionary Computation
- Publication Type :
- Academic Journal
- Accession number :
- 136747847
- Full Text :
- https://doi.org/10.1109/TEVC.2018.2871362