Multi-step evolution strategy based DNA generic algorithm for parameters estimating

A multi-step evolution strategy based DNA genetic algorithm with the multi-step evolution strategy and a new random set crossover with new fitness function is proposed for solving the parameter estimation problem of chemical process. The algorithm adopts DNA encoding and operators. Three new kinds of crossover operators are designed which maintain the diversity of population. A new adaptive mutation rate is also applied to guarantee against stalling at local peak. A new fitness function is designed, which can compare individuals have high similarity. Besides, in order to release the dependence of the range of initial solution on experience set, strengthen the global and local search ability, the multi-step evolution strategy with interrupting genetic, simulated annealing algorithm and parameters interval evolution strategy are developed. Numerical experiment on four typical test functions and heavy oil thermal cracking parameter model are carried out show the efficiency and effectiveness of the proposed algorithms.