.png){kind=icon}
Return
Advancing Power Transformer Design Optimization Using Novel Multi-objective Evolutionary Techniques
.png){kind=icon}
PRE
AI
DOI：10.1007/s13369-026-11599-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multi-objective evolutionary algorithms (MOEAs)
Differential evolution (DE)
Particle swarm optimization (PSO)
Strength Pareto evolutionary algorithm (SPEA)
Non-dominated sorting genetic algorithm (NSGA-II and NSGA-III)
Journal
A
IF:
2.9
Papers:
1.1K
Citations:
0
Organization
