  Binary matrix optimization commonly arise in the real world, e.g.,
multi-microgrid network structure design problem (MGNSDP), which is to minimize
the total length of the power supply line under certain constraints. Finding
the global optimal solution for these problems faces a great challenge since
such problems could be large-scale, sparse and multimodal. Traditional linear
programming is time-consuming and cannot solve nonlinear problems. To address
this issue, a novel improved feasibility rule based differential evolution
algorithm, termed LBMDE, is proposed. To be specific, a general heuristic
solution initialization method is first proposed to generate high-quality
solutions. Then, a binary-matrix-based DE operator is introduced to produce
offspring. To deal with the constraints, we proposed an improved feasibility
rule based environmental selection strategy. The performance and searching
behaviors of LBMDE are examined by a set of benchmark problems.
