大数据共性优化模型的高效算法研究

项目名称： 大数据共性优化模型的高效算法研究

项目编号： No.61472297

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 计算机科学学科

项目作者： 王宇平

作者单位： 西安电子科技大学

项目金额： 82万元

中文摘要： 大数据领域中的很多应用问题可以建成共同类型的优化模型：大规模复杂全局优化模型和超多目标优化模型。这些模型的本质特征是：大规模、复杂（大量局部最优解）、超多目标。已有算法具有如下缺陷：效率低、能力差（难以求出真正的全局最优解，易陷入局部最优解）、对超多目标优化模型难以求出在整个解空间分布均匀的代表解集。本项目对大数据领域各类应用问题经常出现的这些共性模型，研究将大规模问题转化为小规模问题的高效新技术，极大减小求解难度；研究消除大量局部最优解的技术，极大提高求解效率并降低求解难度；研究跳出局部最优解的新技术，保证算法能求出真正全局最优解，为解决大规模复杂全局优化问题提供新的途径。对超多目标优化模型，研究解之间优劣排序的新方法，克服用Pareto最优解概念排序导致最优解数目太多的缺陷；同时研究新的高效聚合函数法，使其能求出反映最优解整体分布的代表解集，克服已有算法难以求出分布好的最优解集的缺陷。

中文关键词： 大数据；共性优化模型；算法设计；超多目标优化；大规模优化

英文摘要： Many big data application problems can be modeled as some widely used common optimization models: the large-scale complex global optimization model and many-objective optimization model. The essential characteristics of these models are: large-scale, too complex (a huge number of local optimal solutions, nonlinear, nonconvex,etc.), and many objectives to be optimized. The shortcomings of the existing algorithms include: low efficiency (long execution time), low ability (very difficult to find global optimal solutions and easy to trap in local optimal solutions), and very difficult to find a representative set of uniformly distributed Pareto optimal solutions along whole Pareto front.In this project, for these common optimization models widely used and often appeared in various big data application problems, we shall focus our research on the following: design new techniques to transform large scale optimization problem into some small scale problems and thus greatly decrease the difficulty of problem solving; propose efficient methods which can eliminate a lot of local optimal solutions and thus greatly enhance the algorithm efficiency and reduce the difficulty of problem solving; design new methods to jump out from current local optimal solutions to other better ones and ensure the algorithm to find global optimal solution. As a result, the project will provide a new way to solve large scale complex global optimization models. For many objective optimization models, design new methods of sorting candidate solutions so as to overcome the drawback that Pareto optimal solution based sorting method will result in too many Pareto optimal solutions. Meanwhile, design new efficient scalarizing function methods so that it can find a representative set of Pareto optimal solutions which can reflect the whole distribution of Pareto front, and overcome the drawbacks that the existing algorithms can not find a representative set of well distributed solutions.

英文关键词： Big data;optimization model;algorithm design;many objective optimization;large scale optimization