Decomposition-Based Multiobjective Optimization is a multiobjective optimization methodology that decomposes a multiobjective problem into a set of single-objective subproblems, which are then optimized simultaneously through mutual information exchange, distinguished from other approaches by its reliance on scalarization techniques to guide the search toward diverse Pareto-optimal solutions.
522
Publications
44.1K
Citations
1.3K
Authors
472
Institutions
Publications per year
2017–2026
301
Variants
Moea/d
Parents
Publications
522
1–5 of 522
Authors
1.3K
- QZ
38
h-index
- CACarlos A. Coello Coello
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico
15 publications · 743 citations
14
h-index
- QL
14
h-index
- HI
12
h-index
- SY
12
h-index
1–5 of 1.3K
Institutions
472
City University of Hong Kong
Hong Kong, Hong Kong
35 h-index
81 publications
12.3K citations
University of Essex
Colchester, United Kingdom
28 h-index
45 publications
27.1K citations
Xidian University
Xi'an, China
26 h-index
116 publications
9.3K citations
Shenzhen University
Shenzhen, China
18 h-index
88 publications
3.8K citations
Xi'an Jiaotong University
Xi'an, China
15 h-index
25 publications
1.5K citations
Northeastern University
Shenyang, China
15 h-index
52 publications
3.1K citations
Southern University of Science and Technology
Shenzhen, China
15 h-index
29 publications
1.3K citations
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional
Mexico City, Mexico
14 h-index
20 publications
1.1K citations
1–8 of 472
Venues
- IEEE Transactions on Evolutionary Computation
59 publications · 22K citations
44
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- IEEE Transactions on Cybernetics
38 publications · 3.6K citations
31
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- Information Sciences
29 publications · 981 citations
20
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- Applied Soft Computing
33 publications · 1.2K citations
19
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- Swarm and Evolutionary Computation
27 publications · 975 citations
18
h-index
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