Desenvolvimento de Operador Matemático para Algoritmos de Otimização Heurísticos Aplicado a Problema de Geoprospecção

Wesley Pacheco Calixto, Thiago Martins Pereira, A. J. Alves, Jesus Carlos da Mota, E. G. Domingues, J. L. Domingos, A. Paulo Mendes Breda Dias Coimbra, Bernardo Pinheiro de Alvarenga

Abstract


O propósito deste trabalho é apresentar um operador genético desenvolvido a partir dos métodos matemáticos de extrapolação de curva. Este operador irá auxiliar na produção de um indivíduo melhor adaptado na população de um algoritmo genético com codificação real, reconhecendo padrões inerentes aos genes dos cromossomos dos melhores indivíduos de cada geração. O operador proposto em conjunto com um algoritmo genético com codificação real é comparado com cinco outros métodos diferentes de otimização aplicados a prospecção geoelétrica.

References


G. E. P. Box; J. S. Hunter. Multifactor experimental designs for exploring response surfaces. Annals of Mathematical Statistics, 28(1):195-241. 1957.

J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI. 1975.

Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, Charlotte, North Carolina, USA. 1992.

F. Herrera; M. Lozano. Adaptive genetic operators based on coevolution with fuzzy behaviors. IEEE Transactions on Evolutionary Computation, v. 5, no.2, 149-165. 2001.

W. P. Calixto; J. C. da Mota; B. P. Alvarenga. Methodology for the Reduction of Parameters in the Inverse Transformation of Schwarz-Christoffel Applied to Electromagnetic Devices with Axial Geometry. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, doi: 10.1002/jnm.804, 2011.

L.M. de Campos; J.A. Gamez; S. Moral. Partial abductive inference in Bayesian belief networks - an evolutionary computation approach by using problem-specific genetic operators. IEEE Transactions on Evolutionary Computation, v.6, no. 2, 105-131. 2002.

S. Y. Yuen; C. K. Chow. A Genetic Algorithm That Adaptively Mutates and Never Revisits. IEEE Transactions on Evolutionary Computation, v. 13, no.2, 454-472. 2009.

W. P. Calixto; E. G. Marra; L. C. Brito; B. P. Alvarenga. A New Methodology to Calculate Carter Factor Using Genetic Algorithms. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol.24 no.4, 387-399, 2010.

F. Herrera; M. Lozano; J. L. Verdegay. Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. Artificial Intelligence Review. vol.12, 265-319. 1998.

M. Serrano. A practical Scheme compiler: User manual for version 3.7a. Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, USA. 2011.

C. De Boor. A practical guide to splines. Springer-Verlag, New York. 2001.

L. L. Schumaker. Spline Functions: Basic Theory. Cambridge Mathematical Library. 2007.

F. N. Fritsch; R. E. Carlson. Monotone Piecewise Cubic Interpolation. SIAM J. Numerical Analysis, vol.17, 238-246. 1980.

D. Kahaner; C. Moler; S. Nash. Numerical Methods and Software. Prentice Hall. 1988.

R. E. Collins. Mathematical Methods for Physicists and Engineers. Dover Publications, New York, 2 ed. 1999.

M. T. Vakil-Baghmisheh; M. Salim. A modified fast marriage in honey bee optimization algorithm. IEEE Telecommunications (IST), 2010 5th International Symposium, 950-955. 2010.

T. Back. Self-adaptation in Genetic Algorithms. Proceedings of the First European Conference on Artificial Life, 263-271. 1992.

H. Park; J. H. Kim. Potential and dynamics-based Particle Swarm Optimization. IEEE World Congress on Computational Intelligence, Evolutionary Computation, 2008. CEC 2008, 2354 -2359. 2008.

J.J. Liang; A.K. Qin; P.N. Suganthan; S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 281-295. 2006.

Q. Anyong. Differential Evolution: Fundamentals and Applications in Electrical Engineering. John Wiley & Sons, Singapore, 2009.

F. A. Wenner. Method of Measuring Earth Resistivity. Bulletin of the National Bureau of Standards, 1916.

W. P. Calixto; L. Martins Neto; M. Wu; K. Yamanaka. Parameters Estimation of a Horizontal Multilayer Soil Using Genetic Algorithm. IEEE Transactions on Power Delivery, vol.25 no.3, 1250-1257, 2010.

F. Dawalibi; C. J. Blattner. Earth Resistivity Measurement Interpretation Techniques. IEEE Transactions on Power Apparatus and Systems, vol.PAS-103 no.2, 374-382, 1984.

E. D. Sunde. Earth conduction effects in transmission systems. MacMilan, New York, 1968.

J. L. del Alamo. A Comparison among Eight Different Techniques to Achieve an Optimum Estimation of Electrical Grounding Parameters in Two-Layered Earth. IEEE Transactions on Power Delivery, vol.8 no.4, 1890-1899, 1993.

J. L. del Alamo. A Second Order Gradient Technique for an Improved Estimation of Soil Parameters in a Two-Layer Earth. IEEE Transactions on Power Delivery, vol.6 no.3, 1166-1170, 1991.

P. J. Lagacé; J. Fortin; E. D. Crainic. Interpretation of Resistivity Sounding Measurements in N-Layer Soil using Electrostatic Images. IEEE Transactions on Power Delivery, vol.11 no.3, 1349-1354, 1996.

I. F. Gonos; I. A. Stathopulos. Estimation of Multilayer Soil Parameters Using Genetic Algorithms. IEEE Transactions on Power Delivery, vol.20 no.1, 100-106, 2005.




DOI: https://doi.org/10.5540/tema.2014.015.02.0177

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.


Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia