合作协同进化算法的变量相关性学习与成组研究

项目名称： 合作协同进化算法的变量相关性学习与成组研究

项目编号： No.61473233

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 彭星光

作者单位： 西北工业大学

项目金额： 82万元

中文摘要： 合作协同进化算法（CCEA）分而治之的特点，赋予了其高效求解复杂问题的能力。然而，要充分发挥该特点，须确保算法问题分解的正确性。本项目旨在利用概率图形模型对变量关系的建模能力，研究CCEA的变量相关性学习及成组问题，为复杂大规模优化问题的高效求解提供技术手段。主要研究内容包括：①研究基于概率图形模型的CCEA框架，使其具有内在学习能力，并在此框架下研究有效的信息补偿策略，消除由问题分解所至信息丢失对算法全局优化能力的影响；②在概率图形模型CCEA框架下，研究贝叶斯网络模型的分散式构建方法，不断获得能够描述问题变量间关系的全局贝叶斯网络；③研究变量成组策略，适时将全局贝叶斯网络分解为若干子网络，并据此进行变量成组和问题分解，使算法能够自动调整对问题的分解形式。④研究算法的数值实验及分析，并针对新概念水下航行器的总体优化设计问题开展实验分析，验证算法解决实际问题的有效性。

中文关键词： 进化计算；合作协同进化；大规模优化；概率图形模型

英文摘要： Benefit by the divide-and-conquer feature, cooperative coevolutionary algorithms (CCEAs) can optimize with high efficiency by dividing a problem into independent components and optimizing them simultaneously. However, whether the problem is properly divided is a key to make full use of the divide-and-conquer feature. The main purpose of this proposal is to provide effective methods for solving large complex optimization problems in the real world. To this end, we use the probabilistic graphical model (PGM) which is powerful for modeling the variables dependency to investigate the methods of learning variables dependency and problem division. There are four issues in this proposal: (1) To design a PGM based CCEA framework so that the resulting CCEA is able to learn the variables' dependency. Besides, an effective information compensation strategy will also be studied to overcome the information loss when dividing the problem into small components, so that the global optimization performance could be guaranteed. (2) To investigate the decentralized Bayesian networks (BNs) learning method within the framework of PGM based CCEA. With this method, the global BN that can describe the relationships of the overall variables could be persistently updated during the evolutionary process. (3) To design the grouping strategy of the variables. At a proper moment, the global BN will be decomposed into some sub-BNs. According to these sub-BNs the variables could be grouped and the self-adaptive problem re-division could be achieved as well. (4) To analyze the proposed algorithms via numerical optimization experiments. Moreover, the problem of system optimization of new concept unmanned underwater vehicles will be used to verify the effectiveness of the proposed algorithms for solving real world applications.

英文关键词： evulutionary computation;cooperative coevolution;large scale optimization;probabilistic graphical model