A Novel Real-coded Quantum-inspired Genetic Algorithm and Its Application in Data Reconciliation

摘要

Traditional quantum-inspired genetic algorithm (QGA) has drawbacks such as premature convergence, heavy computational cost, complicated coding and decoding process etc. In this paper, a novel real-coded quantum-inspired genetic algorithm is proposed based on interval division thinking. Detailed comparisons with some similar approaches for some standard benchmark functions test validity of the proposed algorithm. Besides, the proposed algorithm is used in two typical nonlinear data reconciliation problems (distilling process and extraction process) and simulation results show its efficiency in nonlinear data reconciliation problems.