A Coevolutionary Framework for Constrained Multiobjective Optimization Problems
Anhui University · Nankai University · +2 more institutions
Abstract
Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the…
Citation impact
- FWCI
- 36.73
- Percentile
- 100%
- References
- 69
Authors
5Topics & keywords
- Multi-objective optimization
- Mathematical optimization
- Computer science
- Evolutionary computation
- Constrained optimization
- Optimization problem
- Evolutionary algorithm
- Artificial intelligence
Funding
- RSRoyal SocietyAward: IEC\NSFC\170279
- NNNational Natural Science Foundation of ChinaAwards: 61876123, U1804262, 61822301, 61906001, 61672033, 61590922
- SOSociety of Hong Kong ScholarsAward: XJ2019035
- NSNatural Science Foundation of Anhui ProvinceAwards: 1808085J06, 1908085QF271
- SKState Key Laboratory of Synthetical Automation for Process IndustriesAward: PAL-N201805
- FRFundamental Research Funds for the Central UniversitiesAward: 63192616