Novel Gamma Differential Evolution Approach for Multiobjective Transformer Design Optimization

• Published in: IEEE transactions on magnetics Vol. 49; no. 5; pp. 2121 - 2124
• Main Authors: Coelho, Leandro dos Santos, Mariani, Viviana Cocco, Ferreira da Luz, Mauricio V., Leite, Jean Vianei
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.05.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Computer simulation; Copper; Cross-disciplinary physics: materials science; rheology; Crossovers; Design optimization; Differential evolution; Evolution; Exact sciences and technology; gamma distribution function; Magnetism; Materials science; multiobjective optimization; Mutations; Optimization; Optimization algorithms; Other topics in materials science; Pareto optimality; Physics; Studies; Transformer cores; transformer design optimization; Transformers; Vectors; Windings; Design; multiobjective optimization; Differential evolution; transformer design optimization; gamma distribution function; Magnetic properties
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2013.2243134

The differential evolution (DE) algorithm is a simple but powerful population-based stochastic search technique for solving global optimization problems. DE consists of three main operations: mutation, crossover and selection. The advantages of DE are simple structure, ease of use, speed and robustness. However, to achieve optimal performance with DE, time consuming parameter tuning is essential as its performance is sensitive to the choice of the mutation and crossover values. In this paper, a novel DE algorithm (NDE) based on truncated gamma probability distribution function is proposed for solving multiobjective optimization problems as the design of transformers. Simulations of transformer design optimization (TDO) demonstrate the effectiveness of the proposed optimization algorithm. The simulation results show that, compared with other multiobjective DE algorithm, the proposed NDE is able to find better spread of solutions with better convergence to the Pareto front and preserve the diversity of Pareto solutions more efficiently.