Linguistic information-based granular computing based on a tournament selection operator-guided PSO for supporting multi-attribute group decision-making with distributed linguistic preference relations

作     者：Huang, Ting Tang, Xiaoan Zhao, Shuangyao Zhang, Qiang Pedrycz, Witold 

作者机构：Hefei Univ Technol Sch Management Box 270 Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Anhui Peoples R China Minist Educ Engn Res Ctr Intelligent Decis Making Hefei 230009 Anhui Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2022年第610卷

页      面：488-507页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China, NSFC, (62076182, 62111530056, 72101075, 72101078, 72171069) Natural Science Foundation of Anhui Province, (2108085QG289) Fundamental Research Funds for the Central Universities, (JZ2022HGTB0286, PA2020GDKC0020, PA2021KCPY0030) 

主　　题：Multi -attribute group decision -making Distributed linguistic preference relation Linguistic information granulation Constrained non-linear optimization Particle swarm optimization 

摘      要：This study investigates linguistic information-based granular computing for supporting multi-attribute group decision-making where relative importance of experts/attributes, and outcomes of pairwise comparisons over alternatives are recorded in the form of dis-tributed linguistic preference relations. First, we propose two linguistic information gran-ulation models based on the measures of consistency and consensus to realize the operability of two types of linguistic information, respectively. The granulation of the weight linguistic information is closely linked with that of the alternative preference lin-guistic information in a way that two sets of control factors are introduced. Second, an esti-mation method for expert/attribute weights is developed based on the optimal control factors and solutions to the aforesaid linguistic information granulation models. The pro-posed linguistic information granulation models are in fact constrained non-linear opti-mization problems which may be neither effectively nor efficiently solved by general optimization methods. Third, we therefore propose a novel solution approach named tour-nament selection operator-guided particle swarm optimization (TSOG-PSO). Compared with the standard PSO and its variants, the TSOG-PSO enjoys more feasibility and lower complexity. A new energy vehicle evaluation problem is analyzed using the proposals to demonstrate their applicability. Some discussions and comparative studies are conducted to illustrate the effectiveness of the proposals. (c) 2022 Elsevier Inc. All rights reserved.