Research On DNA Genetic Algorithms And Applications

A genetic algorithm (GA) is a stochastic global optimization technique which simulates natural evolution process, and it has been widely employed in the modeling and optimization problems of chemical engineering processes. But GA has weak local searching ability and tends to premature. Furthermore, the binary encoding method cannot represent the abundant genetic information and reflect the organism control function of genetic information in its computing model, especially the function of the DNA encoding method. Recently, with the development of DNA computing, researchers find that the intelligent systems based on DNA can reflect the genetic information of organism and develop more powerful intelligent methods to solve complex optimization problems.Inspired by the biological characteristics of DNA, DNA genetic algorithms (DNA-GA) and their applications are studied in this dissertation. The main contents are as follows.(1) Inspired by DNA molecular operations, several novel crossover operators are proposed. The convergence of the DNA genetic algorithm with the crossover operators is analyzed in terms of Markov chain model, and the results of the test experiments show that the algorithm can effectively maintain the diversity of the population and reduce the required evolution generations. Then, the algorithm is used to estimate the parameters of a fluid catalytic cracking unit model, and the solution of typical test functions show the model can reflect the dynamic property of the complex process.(2) Inspired by DNA and the expression of genetic information, some novel mutation operators are desigened based on the nucleotide bases encoding method. The convergence of the DNA genetic algorithm with the mutation operators is analyzed in terms of Markov chain model. The results with some typical test functions show the proposed operators can largely increase the comvergence speed of the DNA genetic algorithm, and improve the capability of overcoming the fraudulence. The algorithm is applied to model a hydrogenation reaction, and the results illustrate the effectiveness of the algorithm.(3) Both the novel crossover and mutation operators are adopted in the DNA genetic algorithm. The results of several test functions show that with the help of a proper combination of two kinds of operators, the performance of the DNA genetic algorithm can be improved. This algorithm is applied in the parameter estimation of the heavy oil thermal cracking model, and the results show that a smaller modeling error is reached.(4) A hybrid DNA genetic algorithm is presented for the nonlinear optimization problems with inequality constraints. The hybrid algorithm integrates the global searching ability of DNA genetic algorithm and the local searching ability of SQP. The comparison with typical test functions shows the effectiveness of the proposed hybrid algorithm. The optimal solution for the gasoline-blending scheduling problem gained through the hybrid algorithm shows that the higher profit and the product quality constraints are achieved.(5) A double-chain DNA genetic algorithm (dcDNA-GA) based general regression neural network (GRNN) method is proposed for nonlinear systems. In this method, GRNN optimized by dcDNA-GA is used to the modeling of a delayed coking process. The comparison and simulation results show that the smaller error and AIC indicators are gained.(6) A Chaos DNA genetic algorithm (CDNA-GA) based T-S fuzzy recurrent neural network method for complex nonlinear systems is suggested. In this method, the T-S neural network optimized by the CDNA-GA is used to the modeling of a pH neutralization process. The comparision and simulation results show the feasibility and advantage of this method.(7) A DNA multiobjective genetic algorithm is proposed for multi-objective optimization problems. The results of typical test functions show that the proposed algorithm can converge nearer to the Pareto front, and the spread and the precision of the solution are both improved. This algorithm is used to design a generalized predictive controller for a pH neutralization process, and the simulation results show the higher precision and better performance is obtained.