Abstract
Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is a recently proposed algorithm which is a research focus in the field of multi-objective evolutionary optimization. It decomposes a multi-objective problem into subproblems by mathematic programming methods and applies evolutionary algorithms to optimize the subproblems simultaneously. MOEA/D is good at finding Pareto solutions which are evenly distributed. However, it can be improved for problems with discontinuous Pareto fronts (PF). Many solutions will assemble in breakpoints in this situation. A method for adjusting weight vectors for bi-objective optimization problems with discontinuous PF is proposed. Firstly, this method detects the weight vectors which need to be adjusted using a property of MOEA/D. Secondly, the reserved vectors are divided into several subsets. Thirdly, after calculating the ideal number of vectors in each subset, vectors are adjusted evenly. Lastly, the corresponding solutions are updated by a linear interpolation. Numerical experiment shows the proposed method obtains good diversity and convergence on approached PF.
| Original language | English |
|---|---|
| Pages (from-to) | 3997-4012 |
| Number of pages | 16 |
| Journal | Soft Computing |
| Volume | 22 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Jun 2018 |
| Externally published | Yes |
Keywords
- Adjust weight vector
- Discontinuous Pareto fronts
- MOEA/D
- Multi-objective evolutionary algorithm (MOEA)
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Geometry and Topology
Fingerprint
Dive into the research topics of 'Adjust weight vectors in MOEA/D for bi-objective optimization problems with discontinuous Pareto fronts'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver