文献类型：期刊文献

英文题名：Enhancing population diversity based gaining-sharing knowledge based algorithm for global optimization and engineering design problems

作者：Liang, Ziyuan[1];Wang, Zhenlei[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China

年份：2024

卷号：252

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20241916034616);WOS:【SCI-EXPANDED(收录号:WOS:001239356000001)】；

基金：This work was supported by the National Key Research and De-velopment Program of China (2022YFB3305900) , National Natural Science Foundation of China (Key Program: 62136003) , National Nat-ural Science Foundation of China (62073142, 62173147) , and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Gaining-sharing knowledge based algorithm; Population diversity; Meta-heuristic algorithm; Global optimization; Engineering optimization problems

摘要：Gaining -sharing knowledge based algorithm (GSK) is a recently emerged meta -heuristic algorithm based on human behavior and has been successfully applied to solve various optimization problems. However, GSK tends to get trapped in local optimum due to the rapid loss of population diversity during the optimization process, resulting in an imbalance between exploration and exploitation. To overcome this deficiency, this paper proposes an enhancing population diversity based GSK (EPD-GSK) framework. The proposed EPDGSK framework incorporates three components: (1) The utilization of Sobol sequence with low divergence to initialize the population, enhancing the diversity of initial solutions. (2) The integration of the Cauchy mutation strategy in the junior phase to perturb individuals and expand the search space. (3) The application of the reverse learning update mechanism in the senior phase, increasing the likelihood of escaping local optimum. These techniques promote population diversity throughout the exploration and exploitation stages. The proposed EPD-GSK framework was evaluated on CEC2017, CEC2020, and the latest CEC2022 test suites as well as on four constrained real -world engineering design problems. The experimental results demonstrate that EPD-GSK can effectively improve the performance of various existing GSK algorithms. Furthermore, EPD-GSK also exhibits better performance compared with other state-of-the-art algorithms.