Data-Driven and Decomposition-Based Multiobjective Multitask Optimization for Automotive Shape Design Problem

Evolutionary algorithms have been proven effective in solving complex optimization problems. This paper proposes a production shape optimization framework, and a data-driven and decomposition-based multiobjective multitask evolutionary algorithm with multiple neighbor structures and knowledge types, called MTEA/D-MNK, for complex shape optimization problems. Initially, a 3D point cloud autoencoder is trained via unsupervised learning to extract key design variables across tasks. Subsequently, each task is decomposed into a series of single-objective subproblems using weight vectors. We constructed diverse neighbors and knowledge types for each subproblem to fully exploit beneficial information in both the objective and decision spaces, accelerating the optimization process. Additionally, we proposed an adaptive parameter adjustment strategy to dynamically manage the type and amount of transferred knowledge during different evolutionary stages. The proposed MTEA/D-MNK effectively addresses the critical issues in knowledge transfer: which knowledge to transfer, how to transfer it, and how much to transfer. Finally, we comprehensively test MTEA/D-MNK on nineteen multiobjective multitask optimization (MO-MTO) benchmark instances and apply it to a practical automotive topology shape design problem, using computer simulations to optimize wind resistance coefficients and volumes of both sedan and SUV simultaneously. Experimental results demonstrate that the proposed algorithm significantly outperforms the other five state-of-the-art algorithms, chieving the best performance metrics on 18 of 20 CEC2017 benchmark instances, all 20 CEC2019 instances, and one case study of automotive shape design, as well as the highest rank in the Friedman rank test.