An ensemble game theoretic approach for multi-objective optimization

作     者：Badami, Mahsa Mozafari, Niloofar Hamzeh, Ali Hashemi, Sattar 

作者机构：Shiraz Univ Dept Comp Sci Engn & IT Sch Elect & Comp Engn Shiraz Iran 

出 版 物：《AI COMMUNICATIONS》 (人工智能通讯)

年 卷 期：2015年第28卷第3期

页      面：553-566页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Iranian Telecommunication Research Center (ITRC) [T/500/13266] 

主　　题：Multi-objective clustering game theory ensemble clustering equi-partitioning compactness 

摘      要：Recently multi-objective clustering has been extensively explored due to its appearance of new applications in many domains. However, in many applications, there is more than a single objective which is needed to be optimized in the context of the application, such as facility location, ad hoc networks and sensor networks. These domains must optimize two objectives of compactness and equi-partitioning which may be conflicted in some situations. Existing algorithms have high complexity. In this paper, we propose an Ensemble Game Theoretic approach for multi-objective clustering method which optimizes two objectives of compactness and equi-partitioning, simultaneously. We compare our algorithm on variety of data sets including synthetic and real ones. The remarkable results are very promising and demonstrate the efficiency of presented approach both in performance and complexity.