RNA Genetic Algorithm with Adaptive Crossover Probability for Estimating Parameters of Heavy Oil Thermal Cracking Model

Inspired by the biological RNA, a RNA genetic algorithm with adaptive crossover probability (acRNA-GA) is proposed to estimate parameters of the heavy oil thermal cracking three-lumped model. In acRNA-GA, each individual is represented by nucleotide bases. The crossover operators including competitive permutation operator, translocation operators and inversion operator based on RNA molecular operations are designed to improve the diversity of the population and overcome the premature convergence of GA. An adaptive crossover probability in term of the fitness value is employed to further enhance the global searching ability and the convergence speed. The efficiency of the proposed algorithm is validated by four typical benchmark functions. Finally, acRNA-GA is implemented on parameter estimation of the heavy oil thermal cracking three-lumped model. The results indicate that the proposed algorithm has better performance than that of SGA.