Improvement Of Bbo Algorithm And Its Application In The Dynamic Model Identification And Process Optimization

Biogeography-Based Optimization algorithm is a kind of population-based optimization algorithm inspired by biogeography. The algorithm not only researched biological survival, population decline and extinction but also constructed an intelligent algorithm by simulating the population migration and mutation. The algorithm has the advantage of unique mechanism and characteristic of simple structure and is easy to implement.With the progress of BBO's theoretical study, its application is becoming more and more wide, which attracts the attention of many scholars at home and abroad who join in the research upsurge. However, the performance of the algorithm remains to be improved.In this paper, the author presents an improved Biogeography-Based Optimization algorithm, for BBO algorithm has the phenomenon of easily precocious convergence in the process of optimization. Improved algorithm is on the basis of biogeography, integrating the concept of artificial domestication, the combination of artificial domestication theory and biogeography, to solve the problem, which BBO algorithm has relatively lower ability to explore at the end of the process. In this paper, the improved algorithm is applied to the system identification, controller parameters setting,dynamics modeling of n-butane isomerization chemical reaction and operation optimization, which received good results. So the main research content of this subject is the improvement of BBO algorithm and its application. Summarized as:(1) Biogeography optimization algorithm is improved. Biogeography optimization algorithm converges quickly, but later it is underpowered.Aiming at solving this problem, this paper combines the concept of artificial domestication and biogeography and thus a new improved algorithm is proposed, which is called I-BBO. In order to explore the benefits of I-BBO,we compared its performance on six benchmark functions with five other population-based optimization methods. Simulation experiments show that I-BBO algorithm improved the species diversity, enhanced the search ability of the algorithm and sped up the optimization speed.(2) I-BBO algorithm is applied to realizing system identification. This paper uses second order plus time delay system to approximate complex higher order industrial systems, and a variety of intelligent optimization algorithm is used to identify the parameters. Then, this method is extended to the closed loop system model identification and provides the identifiability of the closed-loop system is given, and many population-based intelligence optimization algorithms are used to complete the closed-loop system identification. The simulation results show that I-BBO has superior performance.(3) I-BBO algorithm is applied to tune PID controller parameters. In this paper, we use two examples of different objects' controller parameter tuning to verify the performance of I-BBO algorithm. Simulation results show that the improved algorithm has superior performance than the old ones in PID controller parameters tuning.(4) I-BBO algorithm is applied to identification of kinetics model parameters of n-butane isomerization reaction and optimization of operation.According to experimental data of n-butane isomerization reaction and chemical reaction networks, the dynamic kinetic model has been established,which is identified by I-BBO algorithm. At the same time, according to the established model, operating conditions of the n-butane heterogeneous chemical reaction are optimized, in theory to guide the chemical reaction process to be carried out in the optimization direction.