Global optimization of thermal conductivity using stochastic algorithms

Abstract

In this article, the performance of a self-organizing migration algorithm (SOMA), a new stochastic optimization algorithm, has been compared with a genetic algorithm with floating-point representation (GAF) and differential evolution (DE) for an engineering application. This application is the estimation of the apparent thermal conductivity of foods at freezing temperature using an inverse method. Assuming two piecewise functions for apparent thermal conductivity in function of the temperature data, the heat diffusion equation was solved to estimate the unknown variables of inverse problem. The thermal conductivity is continuously adjusted by three approaches of stochastic optimization algorithms, used to minimize a performance criterion based on error information for the inverse problem. The variables that provide the best fitness between the experimental and predicted time-temperature curves at centre of the food under freezing conditions were obtained. Moreover, a statistical analysis showed the agreement between reported and estimated curves. In this application domain, the SOMA and DE approaches outperform the GAF.