Integrating metaheuristic and machine learning for optimization of a full-scale transmission line tower

Resumo

In long transmission lines (TLs), the design of a transmission line tower (TLT) can be replicated multiple times, a contrast to most structures that feature a unique design. Various optimization methods have been developed to reduce the overall mass of these structures. Historically, most research has relied on metaheuristic algorithms to identify optimal solutions. More recently, machine learning (ML) techniques have begun to be integrated with metaheuristics to speed up the optimization process, although ML has primarily been applied to smaller TLTs, in academic settings, or to simplify structural analysis, potentially compromising accuracy. This paper introduces a new approach that combines the Backtracking Search Algorithm (BSA), known for its efficacy in similar real-world TLT optimization challenges, with Kriging-based Efficient Global Optimization (EGO). This methodology starts by optimizing the size using BSA and subsequently employs EGO to refine the shape. This dual-step optimization effectively significantly reduces the mass of the TLT with only a few hundred additional objective function evaluations (OFEs). In comparison, simultaneous size and shape optimization using BSA alone requires over one hundred thousand additional OFEs to achieve comparable results, showing the potential of the proposed approach in dealing with expensive engineering design optimization problems.