USE OF GENETIC ALGORITHMS IN THERMAL PROPERTY ESTIMATION: PART I - EXPERIMENTAL DESIGN OPTIMIZATION

Abstract

This two-part study is on the use of genetic algorithms (GAs) to design experiments and develop estimation methodologies for the determination of thermal properties; Part I is focused on the development of an improved GA and the implementation of this algorithm to optimize experimental designs for the estimation of thermal properties, while Part II is directed toward the use of this algorithm in the estimation of thermal properties. In Part I the methodology used in the improved GA, called the extended elitist genetic algorithm (EEGA), is presented, and results from two optimization test problems are compared with those obtained previously from a basic elitist genetic algorithm (BEGA) and a parametric study. GAs are based on the genetic and selection mechanisms of nature, and the EEGA improves on the BEGA by enhancing the Darwinian principle of the “survival of the fittest. ” In the test problems, several key parameters in two experimental designs used for the simultaneous estimation of thermal properties were optimized. The results from these two test problems indicated that the computational efficiency of the EEGA was much higher and the results were slightly better than those of either the BEGA or the parametric study. Overall, genetic algorithms were found to be well suited for use in the optimization of experimental designs for the estimation of thermal properties.

Notes

Address correspondence to Professor Elaine P. Scott, Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Randolph Hall, Blacksburg, VA 24061-0238. E-mail: [email protected]