Advancing Power Transformer Design Optimization Using Novel Multi-objective Evolutionary Techniques

This paper presents a Multi-Objective Differential Evolution framework (MODEA) and an enhanced Multi-Objective Particle Swarm Optimization (MOPSO) technique for power transformer design optimization. The proposed approaches integrate multi-objective optimization mechanisms to improve solution quality, convergence, and diversity of the Pareto-optimal front. A comprehensive comparison is conducted against three widely used Multi-Objective Evolutionary Algorithms (MOEAs), namely the Strength Pareto Evolutionary Algorithm (SPEA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Non-Dominated Sorting Genetic Algorithm III (NSGA-III). The study simultaneously minimizes active-part cost and maximizes transformer efficiency while evaluating convergence, diversity, and Pareto-front quality. Unlike conventional Differential Evolution approaches, MODEA integrates differential mutation with Pareto-based ranking, crowding-distance diversity preservation, and elitist selection mechanisms within a multi-objective optimization framework tailored for constrained transformer design problems. MATLAB-based simulations demonstrate that MODEA consistently produces high-quality and well-distributed Pareto-optimal solutions. Furthermore, the best compromise solution obtained by MODEA achieved an 11.17% reduction in active-part cost compared with the reference transformer design while maintaining the same efficiency level of 99.62%. The results demonstrate the effectiveness of the proposed framework for practical power transformer design optimization and provide valuable insights into the application of evolutionary optimization techniques to complex engineering problems.