求解大规模优化问题的新型协同差分进化算法  

Cooperative differential evolution algorithm for large-scale optimization problems

作　　者：董小刚[1] 邓长寿[1] 谭毓澄[2] 彭虎[1] 吴志健[3] 

机构地区：[1]九江学院信息科学与技术学院,江西九江332005 [2]九江学院理学院,江西九江332005 [3]软件工程国家重点实验室(武汉大学),武汉430072

出　　处：《计算机应用》2017年第11期3219-3225,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61364025);江西省教育厅科技项目(GJJ161072;GJJ161076)~~

摘　　要：基于分而治之的策略,研究求解大规模优化问题的新方法。首先,基于加性可分性原理提出一种改进的变量分组方法,该方法以随机取点的方式,成对检测所有变量之间的相关性;同时,充分利用相关性学习的信息,对可分变量组进行再次降维;其次,引入改进的差分进化算法作为新型子问题优化器,增强了子空间的寻优性能;最后,将两项改进引入到协同进化框架构建DECC-NDG-CUDE算法。在10个选定的大规模优化问题上进行分组和优化两组仿真实验,分组实验结果表明新的分组方法能有效识别变量的相关性,是有效的变量分组方法;优化实验表明,DECCNDG-CUDE算法对10个问题的求解相对于两种知名算法DECC-DG、DECCG在性能上具备整体优势。A new method of large-scale optimization based on divide-and-conquer strategy was proposed. Firstly, based on the principle of additive separability, an improved variable grouping method was proposed. The randomly accessing point method was used to check the correlation between all variables in pairs. At the same time, by making full use of the interdependency information of learning, the large groups of separable variables were re-grouped. Secondly, a new subcomponent optimizer was designed based on an improved differential evolution algorithm to enhance the subspace optimization performance. Finally, this two kinds of improvements were introduced to co-evolutionary framework to construct a DECC-NDG-CUDE （Cooperative differential evolution with New Different Grouping and enhancing Differential Evolution with Commensal learning and Uniform local search） algorithm. Two experiments of grouping and optimization were made on 10 large-scale optimization problems. The experimental results show the interdependency between variables can be effectively identified by the new method of grouping, and the performance of DECC-NDG-CUDE is better than two state-of-the-art algorithms DECC-D （ Differential Evolution with Cooperative Co-evolution and differential Grouping） and DECCG （ Differential Evolution with Cooperative Co-evolution and Random Grouping）.

关 键 词：大规模优化 变量分组 加性可分 优化器 协同进化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]