A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

作     者：Ou, Junwei Xing, Lining Liu, Min Yang, Lihua 

作者机构：Cent South Univ Forestry & Technol Sch Transportat & Logist Changsha 410004 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411105 Peoples R China Qingdao Hengxing Univ Sci & Technol Qingdao 266100 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2020年第8卷

页      面：13362-13374页

核心收录：

基　　金：National Natural Science Foundation of China [61773120, 61873328] National Natural Science Fund for Distinguished Young Scholars of China Scientific Research Fund of Hunan Provincial Education Department [18K060] Foundation for the Author of National Excellent Doctoral Dissertation of China [2014-92] 

主　　题：Dynamic multi-objective optimization evolutionary algorithms change degree diversity 

摘      要：Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods.