Due to the curse of dimensionality caused by the increasing number of objectives, it is very challenging to tackle many-objective optimization problems (MaOPs). Aiming to alleviate the loss of selection pressure in the fitness evaluation for MaOPs, this paper proposes a novel evolutionary optimization framework, called Tk-MaOEA, based on transfer learning assisted by Kriging model. In this approach, in order to achieve global space optimization, transfer learning is used as a map tool to reduce the objective space, i.e., devising transfer matrix to simplify the optimization process. For the objective optimization, the Kriging model is appropriately incorporated in order to further reduce computation cost. Accordingly, any EA-based paradigm or search strategy can be integrated into this framework. Fast non-dominated sorting and farthest-candidate selection (FCS) methods are used to guarantee the diversity of non-dominated solutions. Comprehensive evaluations on a set of benchmark functions have been conducted to show that the proposed Tk-MaOEA is efficietive for solving complex MaOPs.
|Publication status||Accepted/In press - 11 Jan 2019|
- Evolutionary algorithm
- Many-objective optimization
- Transfer matrix
- Kring model