Optimization technique based on evolutionary algorithm and dynamic Bayesian network

A new optimization technique for dynamic system is proposed to achieve autonomous control under complicated environment. Firstly, dynamic Bayesian network (DBN) is incorporated into evolutionary algorithm as a transfer network from t to t + 1 generation. Through DBN, the original static optimization process of evolutionary algorithm based on Bayesian optimization algorithm (BOA) is effectively changed into the dynamic process. Using this scheme, the DBN transfer network can re-establish the optimization direction for system to adapt to various changes of environment. The scheme can help agent to complete a series of complex tasks without intervention from users. The experimental results clearly demonstrate the accuracy and effectiveness of the method. Secondly, new concepts are introduced to increase the optimization speed and meet the real-time requirement. One is restriction function, which is used to cut off unnecessary nodes during evolutionary computation, and the other is replacement, which is used to inherit part of good results of former generation evolutionary. The new concepts are used to make the evolutionary optimization process more efficient.