Continuous Optimization Algorithm for PCB Layout Based on Differential Evolution

In Printed Circuit Board (PCB) design, effective layout optimization is crucial for electrical performance, thermal management, and signal integrity. However, due to the inherently complex, high-dimensional nature of PCB layout problems, traditional optimization methods often struggle to deliver satisfactory solutions. This paper presents a continuous optimization algorithm for PCB layout based on Differential Evolution (DE). As a global search algorithm, DE effectively explores complex continuous optimization spaces through mutation operations based on the differences among individual solutions, enabling the identification of optimal layouts. We begin by formulating the PCB layout problem as a constrained multi-objective optimization issue. Subsequently, we enhance the DE algorithm's adaptability by introducing an adaptive mutation operator, which improves the balance between global search and local exploitation. To validate the effectiveness of the proposed algorithm, we conducted experiments using a series of real-world PCB design cases. The results demonstrate that the DE-based PCB layout optimization algorithm outperforms traditional methods across multiple performance metrics, particularly in high-dimensional complex scenarios, showcasing superior global optimization capabilities and convergence speed.