Genetic Algorithm And Its Yeast Propagation System

Genetic algorithm is a global optimization algorithm which is based on natural selection and genetic mechanism, and can be used to optimize the complicated system. It doesn't rely on gradient information, and has a high degree of parallelism and a good robustness.Because of extremely broad range of applications and good results in many areas, it becomes a hot issue for scholars and engineers in recent years.Although genetic algorithm achieves good effects in some problems, many shortcomings and deficiencies are exposed along with its superiority, such as premature convergence and bad local searching ability and so on. In order to solve this problem, a hybrid genetic algorithm which introduces the steepest descent method into the genetic algorithm is proposed. Meanwhile, through the combination of a new constraints-handling technique and genetic algorithm, a novel genetic algorithm for solving constraint optimization problem is proposed in this paper. Finally, the new hybrid algorithm is used to optimize the parameters of the PID controller in temperature control of beer yeast spread cultivation system. The main contribution and work are described as following:(1)A hybrid genetic algorithm based on steepest descent method is proposed. This algorithm combines the advantages of the local research ability of steepest descent method and the global research ability of genetic algorithm which improves the convergence speed and convergence precision of the algorithm. Meanwhile, the population diversity is well maintained through simplex crossover and uniform mutation, which solves the problem of the premature convergence of the traditional genetic algorithm. The hybrid algorithm is tested on six benchmark function, and the result demonstrates that the performance of the hybrid algorithm is better.(2) A new genetic algorithm for solving constrained optimization problem is proposed. The constraints are handled by the arithmetic crossover of the feasible solutions and the infeasible solutions, so a trouble which is caused by introducing the penalty factor is avoided. The feasible solutions still remain feasible by introducing bounded mutation. Besides, the reseach ability of algorithm is strengthened and the problem of trapped in local optimal solution is solved by using dimension mutation. At last, efflcientcy and robustness of algorithm is tested on seven benchmark function.(3)According to the control demands of beer yeast spread cultivation system, the scheme of temperature control is designed. PID controller is optimized by using the hybrid genetic algorithm based on established model for controlled object. Simulation results demonstrator that the algorithm has a better effect.