  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.
