Knee Points based Transfer Dynamic Multi-objective optimization Evolutionary Algorithm

Zhenzhong Wang, Zhongrui Mei, Min Jiang, Gary G. Yen

Abstract: When dynamic multi-objective optimization evolutionary algorithms (DMOEA) are used to solve real world problems, these are not only required to be able to find the Pareto-Optimal Set (POS) quickly, but also the results obtained can be easily used by decision makers. The classic DMOEAs have much room for improvement in both aspects. Recently, the transfer learning based DMOEAs have been proved that these methods can significantly improve the quality of the solution, but there are still too many individuals in the POS obtained by these algorithms. The resulting problems are twofold: this not only consumes a lot of computing resources to those solutions that will not be used, but also makes it more difficult for decision makers to choose. In this paper, we proposed a dynamic multiobjective optimization evolutionary algorithm which combines knee solutions with transfer learning method, and the feature of the proposed method is that it only outputs a very small number of solutions, which can greatly improve the efficiency of decisionmaking. The proposed algorithm divides the whole decision space into different subspaces, and find a local knee solutions in each subspace, then a transfer learning framework, Tr-DMOEA, is used to predict the knee solutions of the optimization problem at the next moment by using the local knee solutions and a global knee solution. The experimental results show the effectiveness of our design.