Adaptive Transfer Learning Assisted Multimodal Multi-objective Optimization Algorithm Based on Zoning Search

Zoning search strategies have been utilized to solve multimodal multi-objective optimization problems (MMOPs). However, effectively transferring knowledge among subspaces and mitigating the negative transfer remain significant challenges. To this end, we propose an adaptive transfer learning-assisted zoning search (ZSATL) method to assist other multimodal multi-objective evolutionary algorithms (MMOEAs) in obtaining more equivalent Pareto optimal solutions and a high-quality Pareto front approximation. In the ZSATL, the zoning search is employed to segment the search space into many subspaces. Moreover, an adaptive transfer learning method is proposed to alleviate the negative transfer issue. If the distribution of solution sets in two subspaces is similar, the transfer learning method is employed to exchange knowledge. Otherwise, the brain storm optimization algorithm is employed to find promising regions for mining useful knowledge. The performance of the proposed algorithm is compared with that of seven advanced MMOEAs on balanced and imbalanced MMOPs. Based on the experimental results, the ZSATL can locate more equivalent Pareto optimal solutions in the decision space and find a better PF approximation when compared with other competitors.