中文摘要：

提出一种复杂环境下自主控制的动态优化新方法、首先，利用动态贝叶斯网络作为进化算法t代到t＋1代的转移网络，将儿叶斯优化及概率模型进化算法的静态优化机制推广到动态系统．通过感知环境变化，转移网络可以适时改变优化的基本条件和重新确市优化方向，指导自主智能体在无人干预下顺利完成一系列复杂任务．仿真结果表明基本思路正确．其次，为提高优化速度，满足实时性要求，提出“约束函数”及“置换”的概念，通过减少进化过程中不必要的网络节点及继承上一代部分优良解的方式，使得进化优化不必每次都重头开始，提高算法效率．

英文摘要：

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 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 method. Secondly, new concepts are introduced to increase optimization speed and meet 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 .