A Comprehensive Review of Transformer Design Optimization Using Evolutionary Algorithms: Advances, Challenges, and Future Directions

Transformers are essential components of electrical power systems, representing a significant capital investment and requiring optimized designs to balance efficiency, cost, and performance. As the demand for energy-efficient and cost-effective solutions increases, transformer manufacturers face increasing pressure to enhance design efficiency while minimizing production costs. Traditional deterministic optimization techniques often struggle with the high-dimensional, nonlinear, and multi-objective nature of transformer design problems, necessitating the adoption of advanced evolutionary optimization strategies. In recent years, evolutionary algorithms (EAs) have emerged as powerful tools for addressing these challenges, offering adaptability, scalability, and robustness in optimizing transformer designs. This paper presents a comprehensive review of transformer design optimization using EAs, investigating both single and multi-objective approaches applied to distribution, power, and other transformer types. A wide range of publications are reviewed, categorized, and analyzed to provide a structured overview of recent advancements in Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and hybrid AI-assisted approaches. This study highlights key research gaps, including oversimplification of transformer models, limited experimental validation, and insufficient focus on multi-objective optimization. Future directions emphasize the integration of finite element analysis (FEA) into optimization loops, AI-assisted optimization frameworks, and sustainability-oriented transformer designs. These insights provide valuable guidance for both researchers and practitioners, contributing to the advancement of cost-effective, high-efficiency, and industry-applicable transformer design methodologies using evolutionary algorithms.