  Large-scale multiobjective optimization problems (LSMOPs) refer to
optimization problems with multiple conflicting optimization objectives and
hundreds or even thousands of decision variables. A key point in solving LSMOPs
is how to balance exploration and exploitation so that the algorithm can search
in a huge decision space efficiently. Large-scale multiobjective evolutionary
algorithms consider the balance between exploration and exploitation from the
individual's perspective. However, these algorithms ignore the significance of
tackling this issue from the perspective of decision variables, which makes the
algorithm lack the ability to search from different dimensions and limits the
performance of the algorithm. In this paper, we propose a large-scale
multiobjective optimization algorithm based on the attention mechanism, called
(LMOAM). The attention mechanism will assign a unique weight to each decision
variable, and LMOAM will use this weight to strike a balance between
exploration and exploitation from the decision variable level. Nine different
sets of LSMOP benchmarks are conducted to verify the algorithm proposed in this
paper, and the experimental results validate the effectiveness of our design.
