This paper proposes a multiobjective multitasking optimization evolutionary algorithm based on decomposition with dual neighborhood. In our proposed algorithm, each subproblem not only maintains a neighborhood based on the Euclidean distance among weight vectors within its own task, but also keeps a neighborhood with subproblems of other tasks. Gray relation analysis is used to define neighborhood among subproblems of different tasks. In such a way, relationship among different subproblems can be effectively exploited to guide the search. Experimental results show that our proposed algorithm outperforms four state-of-the-art multiobjective multitasking evolutionary algorithms and a traditional decomposition-based multiobjective evolutionary algorithm on a set of test problems.
@article{arxiv.2101.07548,
title = {Multiobjective Multitasking Optimization Based on Decomposition with Dual Neighborhoods},
author = {Xianpeng Wang and Zhiming Dong and Lixin Tang and Qingfu Zhang},
journal= {arXiv preprint arXiv:2101.07548},
year = {2021}
}