A Diversity-Enhanced Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithm
- Authors
- Lin, Qiuzhen; Jin, Genmiao; Ma, Yueping; Wong, Ka-Chun; Coello, Carlos A. Coello; Li, Jianqiang; Chen, Jianyong; ZHANG, Jun
- Issue Date
- Aug-2018
- Publisher
- IEEE Advancing Technology for Humanity
- Keywords
- Decomposition; multiobjective optimization; resource allocation (RA); solution density
- Citation
- IEEE Transactions on Cybernetics, v.48, no.8, pp 2388 - 2501
- Pages
- 114
- Indexed
- SCI
SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Cybernetics
- Volume
- 48
- Number
- 8
- Start Page
- 2388
- End Page
- 2501
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116328
- DOI
- 10.1109/TCYB.2017.2739185
- ISSN
- 2168-2267
2168-2275
- Abstract
- The multiobjective evolutionary algorithm (MOEA) based on decomposition transforms a multiobjective optimization problem into a set of aggregated subproblems and then optimizes them collaboratively. Since these subproblems usually have different degrees of difficulty, resource allocation (RA) strategies have been reported to enhance performance, attempting to dynamically assign proper amounts of computational resources for the solution of each of these subproblems. However, existing schemes for decomposition-based MOEAs fully rely on the relative improvement of the aggregated functions to do this. This paper proposes a diversity-enhanced RA strategy for this kind of MOEA, depending on both relative improvement on aggregated function value and solution density around each subproblem to assign computational resources. Thus, one subproblem surrounded with fewer solutions in its neighboring area and more relative improvement on the aggregated function value will be allocated a higher probability for evolution. Our experimental results show the advantages of our proposed strategy over two popular RA strategies available for decomposition-based MOEAs, on tackling a set of complicated benchmark problems. © 2017 IEEE.
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