Research And Application Of Big Mutation Hybrid Genetic Algorithm

In this paper we investigate the application problem of genetic algorithm to function optimization, production precedence and automation. We study multi-maxim function in the domain of function optimization, Job-shop in the domain of production precedence and the PID parameters selecting in the domain of automation.For solving multi-maxim function, we comes up with a hybrid genetic algorithm through analyzing characteristics of pattern search and genetic algorithm. This hybrid algorithm take advange of local search of pattern search and whole search of genetic algorithm. It first confines the direction of large scale search to the area of high fitness by genetic algorithm, and then utilizes pattern search to search in the small area got by the searching of genetic algorithm., thereby the optim value is got. This hybrid algorithm applicates pattern search on each individual afer crossover operator and mutation operator of genetic algorithm, and improve the mutation operator to the big mutation operator in order to speeding convergence. The paper expatiate on the design and realizing of the hybrid algorithm, and present the flow chart of the program programmed with MATLAB. Through studying the simulation results and compared with the other algorithms, the big mutation hybrid algorithm improves convergence accuracy and convergence probability and reduce convergence generation, therefore it is worth further researching.For the job-shop problem, the operator selecting and algorithm realization based on working procedure coding and based on job coding is investigated in the paper. Some key algorithm such as fitness evaluation function is given in detail.Genetic algorithm is employed to search for the optimized parameters of PID controller applied to ship controling. According to ITAE criterion which appraises system response performance, the value of ITAE is got by simulation of system in the allowable range of PID parameters, and then the transformation of the value of ITAE is adopted to be fitness function evaluating fitness of PID parameters. Better solutions in the allowable range of parameters are gained through searching by genetic algorithm.