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Adaptive Replacement Strategies for MOEA/D.
- Source :
- IEEE Transactions on Cybernetics; Feb2016, Vol. 46 Issue 2, p474-486, 13p
- Publication Year :
- 2016
-
Abstract
- Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem into a set of simple optimization subproblems and solve them in a collaborative manner. A replacement scheme, which assigns a new solution to a subproblem, plays a key role in balancing diversity and convergence in MOEA/D. This paper proposes a global replacement scheme which assigns a new solution to its most suitable subproblems. We demonstrate that the replacement neighborhood size is critical for population diversity and convergence, and develop an approach for adjusting this size dynamically. A steady-state algorithm and a generational one with this approach have been designed and experimentally studied. The experimental results on a number of test problems have shown that the proposed algorithms have some advantages. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 46
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 112286229
- Full Text :
- https://doi.org/10.1109/TCYB.2015.2403849