HS-ES-DE: HS-ES Followed by L-SHADE-EpSin for Real Parameter Single Objective Optimization

• 作者： Jianjun Jiang ( 1 ) ; Yong Wan ( 1,2 ) ; Chengjun Li ( 1,2 ) ; Chuchuan Cen ( 1 ) ; Yikai Shao ( 1 ) ; Qinxue Meng ( 3 ) ;
• 作者单位： 1.School of Computer Science, China University of Geosciences, Wuhan 430074, P.R. China; 2.Hubei Key Laboratory of Intelligent Geo-Information Processin, China University of Geosciences, Wuhan 430074, P.R. China; 3.School of Software, University of Technology Sydney, Sydney 2000, Australia;
• 提交时间：2022-12-07

英文摘要:For real parameter single objective optimization, Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) both perform powerfully. Nevertheless, in the field of real parameter single objective optimization, it is impossible for a given algorithm to perform well in all fitness landscapes. Practice has proved that ensemble of different algorithms may lead to improvement in solution. In this paper, based on two famous population-based metaheuristics - LSHADE-EpSin and HS-ES, we propose ensemble with successively executed constituent algorithms - HS-ES-DE. In our algorithm, HS-ES is replaced by L-SHADE-EpSin after stagnation is detected. Beside our HS-ES-DE, 12 population-based metaheuristics are involved in our experiments in which three benchmark test suites are employed. Experimental results show that our algorithm is very competitive.

• 来自： 李程俊
• 分类： 计算机科学 >> 计算机软件
• 说明： 本文已经投给Information Sciences一年零十个月，仍然没有消息。
• 引用： ChinaXiv:202212.00065 (或此版本 ChinaXiv:202212.00065V1)
  (DOI:10.12074/202212.00065V1)
  (CSTR:32003.36.ChinaXiv.202212.00065.V1)
• 推荐引用方式：Jianjun Jiang,Yong Wan, Chengjun Li,Chuchuan Cen, Yikai Shao,Qinxue Meng.(2022).HS-ES-DE: HS-ES Followed by L-SHADE-EpSin for Real Parameter Single Objective Optimization.CSTR:32003.36.ChinaXiv.202212.00065.V1 (点此复制)