A hybrid evolutionary programming approach for optimal worst case tolerance design of magnetic devices

This paper presents a hybrid evolutionary programming approach to solve the worst case tolerance design problem (WCTDP) in magnetic devices. The hybrid algorithm is formed by a basic evolutionary programming approach, mixed with a gradient-guided local search. Two different local searches procedures are tested in the paper, both specially designed to be effective in the WCTDP. Simulations on an example in the design of a magnetic circuit and comparison with several existing bio-inspired heuristics are carried out, and have shown the goodness of our algorithm.

[1]  Kumar Chellapilla,et al.  Local search operators in fast evolutionary programming , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[2]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[3]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[4]  Amit Konar,et al.  Automatic image pixel clustering with an improved differential evolution , 2009, Appl. Soft Comput..

[5]  Nicola Femia,et al.  Genetic optimization of interval arithmetic-based worst case circuit tolerance analysis , 1999 .

[6]  Jean-Yves Dantan,et al.  Worst-case and statistical tolerance analysis based on quantified constraint satisfaction problems and Monte Carlo simulation , 2009, Comput. Aided Des..

[7]  Amin Nobakhti,et al.  A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier , 2008, Appl. Soft Comput..

[8]  Kumaraswamy Ponnambalam,et al.  A unified approach to statistical design centering of integrated circuits with correlated parameters , 1999 .

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  G. Spagnuolo Worst case tolerance design of magnetic devices by evolutionary algorithms , 2003 .

[11]  James Smith,et al.  A tutorial for competent memetic algorithms: model, taxonomy, and design issues , 2005, IEEE Transactions on Evolutionary Computation.

[12]  S.L. Ho,et al.  A particle swarm optimization method with enhanced global search ability for design optimizations of electromagnetic devices , 2006, IEEE Transactions on Magnetics.

[13]  Xin Yao,et al.  Evolutionary programming using mutations based on the Levy probability distribution , 2004, IEEE Transactions on Evolutionary Computation.

[14]  ChunXia Zhao,et al.  Particle swarm optimization with adaptive population size and its application , 2009, Appl. Soft Comput..

[15]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[16]  Robert Spence,et al.  Tolerance Design of Electronic Circuits , 1997 .

[17]  Giovanni Spagnuolo An interval arithmetic-based yield evaluation in circuit tolerance design , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[18]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[19]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[20]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .