Application of a genetic simulated annealing algorithm for data reconciliation

Abstract

To elucidate the propagation of error or bias from the measurement of a process, the data requires reconciliation. In this article, a genetic simulated annealing algorithm (GSA) based program is proposed for solving data reconciliation (DR) problems. The proposed GSA utilizes simultaneous simulated annealing and modified cross-generational probabilistic survival selection (CPSS) in a genetic algorithm. Validation is performed with linear and nonlinear DR problems. The test starts with the study of appropriate GSA parameters of constraint problems. The performance of GSA with the appropriate parameters is then compared to the genetic algorithm (GA) method, the specific method, and the commercial software DATACON. The proposed GSA, with its ability to give more accurate reconciled data, is a promising choice as an optimization tool for data reconciliation problems.