文献类型：期刊文献

英文题名：Effective Resource Allocation in Cooperative Co-evolutionary Algorithm for Large-Scale Fully-Separable Problems

作者：Du, Wei[1]; Tong, Le[2]; Tang, Yang[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, Shanghai, China; [2] Shanghai Normal University, College of Information, Mechanical and Electrical Engineering, Shanghai, China

年份：2020

卷号：2020-October

起止页码：4198

外文期刊名：Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

收录：EI(收录号：20210209742850)

基金：This research was supported in part by the National Natural Science Foundation of China under Grant No. 61703163, in part by the China Postdoctoral Science Foundation under Grant No. 2018T110356, and in part by Shanghai Sailing Program under Grant No. 19YF1437100.

语种：英文

外文关键词：Evolutionary algorithms

摘要：This paper investigates the effective computational resource allocation for large-scale fully-separable problems under the framework of a cooperative co-evolutionary algorithm called MLSoft. According to different subgroup sizes of the problems, we allocate different numbers of iterations to the subproblems in all the cycles. For high-dimensional subproblems, more iterations are needed during the optimization process; while for low-dimensional subproblems, fewer iterations will be assigned. The experimental results reveal that the proposed resource allocation scheme is simple but effective, which can enhance the performance of MLSoft in solving large-scale fully-separable problems. In addition, we conduct a group of experiments to evaluate the results if a higher weight is assigned to more recent performance in MLSoft. The results show that introducing weight to the latest reward affects very little on the performance of MLSoft. ? 2020 IEEE.