System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Novel Gamma Differential Evolution Approach for Multiobjective Transformer Design Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Coelho, L.D.S. ; Ind. & Syst. Eng. Grad. Program (PPGEPS), Pontifical Catholic Univ. of Parana, Curitiba, Brazil ; Mariani, V.C. ; Ferreira da Luz, M.V. ; Leite, J.V.

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.

Published in:

Magnetics, IEEE Transactions on  (Volume:49 ,  Issue: 5 )