文献类型：期刊文献

英文题名：An opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization

作者：Dai, Mingzhi[1,2];Feng, Xiang[1,2];Yu, Huiqun[1,2];Guo, Weibin[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai 200237, Peoples R China

年份：2023

卷号：259

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20224613116588);WOS:【SCI-EXPANDED(收录号:WOS:000935578900019)】；

基金：This work is supported by the National Natural Science Foundation of China (No. 62276097) , Key Program of Na- tional Natural Science Foundation of China (No. 62136003) , National Key Research and Development Program of China (No. 2020YFB1711700) , Special Fund for Information Development of Shanghai Economic and Information Commission (No. XX-XXFZ- 02-20-2463) and Scientific Research Program of Shanghai Science and Technology Commission (No. 21002411000) .

语种：英文

外文关键词：Biogeography -based algorithm; Emotional preference and migratory  behavior; Differential evolution (DE) strategy; Opposition -based learning; Data clustering

摘要：Clustering has grown to be a research focus in recent years owing to the challenges of labeling massive collected data. Recent advances such as the emotional preference and migratory behavior clustering model jointly utilized the biogeography-based idea and inertia theorem for handling data clustering tasks. However, as with most clustering methods, the initialization for the solution is often purely random. Moreover, the search results vulnerably converge to local optima due to favorable exploration but short exploitation, which requires an additional process to balance them effectively. Inspired by the differential evolution (DE) strategy and the opposition-based learning (OBL) method, the paper proposed a novelty opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization named OBDE-EPMC. Specifically, we first utilize the OBL method to initialize the population to obtain a closer initial individual to the optimal solution. Thereafter, to ensure population diversity during the optimization, we applied the differential evolution (DE) strategy to expand the scope of the solution and the opposition-based learning method to generate an opposite one. Lastly, the historical solution scheme is added to retain the excellent solution within the population. In addition, the theoretical analysis and convergence property proof of OBDE-EPMC are presented. Numerous experiments were performed to compare the proposed OBDE-EPMC with the other seven clustering algorithms on a host of standard datasets. And several standards demonstrate the rationality and effectiveness of our proposed OBDE-EPMC algorithm.(c) 2022 Elsevier B.V. All rights reserved.