Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

TL;DR

This paper introduces LBMDE, a novel differential evolution algorithm designed for large-scale, constrained binary matrix optimization problems like multi-microgrid network design, demonstrating improved solution quality and efficiency.

Contribution

The paper presents a new heuristic initialization, a binary-matrix-based DE operator, and an improved feasibility rule for environmental selection, enhancing optimization in complex microgrid network design.

Findings

LBMDE outperforms traditional methods on benchmark problems.

The proposed algorithm effectively handles large-scale, constrained binary optimization.

Results show improved solution quality and convergence speed.

Abstract

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.