基于协同学习进化多目标优化的网络结构分析

项目名称： 基于协同学习进化多目标优化的网络结构分析

项目编号： No.61473215

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： 屈嵘

作者单位： 西安电子科技大学

项目金额： 83万元

中文摘要： 深入分析基于单目标优化的网络社区检测方法在解决多分辨网络社区检测时的局限性，将多分辨网络社区检测建模为多目标优化问题，建立适用于多分辨网络社区检测的局部学习及其自适应协同策略，建立适用于多分辨网络社区检测的参数自适应学习模型，提出基于进化多目标优化的多分辨网络社区检测方法。分析不同目标函数对社区检测结果的影响，分析不同的编码策略对搜索空间和求解结果的影响，分析局部学习和自适应学习对算法性能的影响。进一步研究基于多分辨网络社区检测的网络拓扑结构优化方法，提高网络的鲁棒性；研究基于多分辨网络社区检测的个性化推荐算法，提高推荐算法的准确性和多样性。研究成果拟在本领域国际主流刊物和知名会议上发表论文12～15篇，申报国家发明专利2～3项，培养博士、硕士5～6人。

中文关键词： 多目标优化；进化算法；复杂网络；社区检测

英文摘要： After a deep analysis of the limitations brought by the single objective optimization based community detection methods for solving multi-resolution network community detection problem, the multi-resolution network community detection problem will be modeled as multiobjective optimization problems. The local learning tactics, the adaptive collaborative learning strategies and the parameter adaptive learning models will be established, and multi-resolution network community detection methods based on multiobjective optimization will be proposed. By analyzing the effect of different objectives on the performance of community detection in networks, the influence of different coding strategies on search space and the effect of different local search and adaptive collaborative learning strategies on the performance of the proposed algorithm will be studied. Network topology optimization methods based on multi-resolution community detection will be proposed to improve the robustness of networks, and personalized recommendation algorithms based on multi-resolution community detection are devised to improve recommendation accuracy and diversity.We will publish 12-15 papers in related leading journals and conferences, apply 2-3 patents, and bring up 5-6 graduate students.

英文关键词： Multi-objective Optimization;Evolutionary Algorithm;Complex Network;Community Detection