Zoning Search and Transfer Learning-based Multimodal Multi-objective Evolutionary Algorithm

Multimodal multi-objective optimization (MMO) not only finds a good Pareto front (PF) approximation in the objective space, but also locates sufficient equivalent Pareto optimal solutions in the decision space. Although the zoning search (ZS) can improve the population diversity and reduce the problem complexity, it searches each subspace independently. This may waste computational resources. To alleviate the above issue, a zoning search and transfer learning- based multimodal multi-objective evolutionary algorithm (called ZSTL-MMOEA) is proposed in the present study. In the ZSTL-MMOEA, the decision space is divided into many subspaces and the transfer learning is used to realize knowledge sharing between two the most similar subspaces. The ZSTL-MMOEA is compared with five recently proposed multimodal multi-objective evolutionary algorithms (MMOE- As) on 22 test functions. Experimental results show that the proposed algorithm outperforms its competitors in most functions.