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This paper uses a new bionic algorithm in turning of PID parameters. The Paddy Field Algorithm (PFA) is operated in the parametric space from the initial scattering of seeds. The number of seeds of every plant depends on the function value, as the plant, the closest optimal solution, produces the most seeds. The produced seeds also depend on the number of the plant's neighbors, for only a percentage is viable due to pollination. Moreover, the seeds of each plant must be dispersed in order to prevent being stuck in local minima. We thus applied this improved algorithm to design the PID controller of a high-order system. The effect of various parameters on the performance of the algorithm was also studied. Its performance is tested with the PID and PSO algorithms. The results show that the approach is effective and the designed controller has a better performance of overshoot and settling time.

With regard to the issue of selecting Radial Basis Functions (RBF) neural network center parameters, this paper has introduced the paddy field algorithm (PFA) for its optimization. PFA had stronger global search capacity and higher convergence speed so as to better optimize RBF neural network. In the simulation experiment, this method was applied to approximation and prediction of a typical nonlinear function and compare with PSO (Particle Swarm Optimization) algorithm and the methodology of training by traditional gradient descent algorithm. The experiment showed that all predicted errors were lower than that of PSO predicted results.