Multifactorial evolutionary algorithm with embedded self-learning strategy

被引：0
|
作者：
Cao L. [1 ,2 ]
Xu R. [1 ,2 ]
机构：
[1] Department of Automation, Xiamen University, Xiamen
[2] Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen University, Xiamen
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 06期
关键词：
Multi-task optimization; Multifactorial evolutionary algorithm; Quasi-Newton method; Random sampling embedding; Self-learning strategy;
D O I：
10.13245/j.hust.210602
中图分类号：
学科分类号：
摘要：
In order to solve the shortcomings of multifactorial evolutionary algorithm (MFEA) of insufficient local search ability, a self-learning strategy based on the quasi-Newton method was proposed to be embedded in the MFEA. Combining the characteristics of MFEA, three types of embedding strategies were designed and applied to the solution of multi-task optimization problems. Experimental results show that the performance of embedded MFEA is far superior to the adaptive memetic algorithm-multifactorial evolution algorithm (AMA-MFEA), which is also improved based on the quasi-Newton method. Research shows that under the condition of the same number of iterations, subpopulation embedding can exert the best effect;under the constraints of the same optimization time, random sampling embedding is the most effective. © 2021 Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：7 / 12
页数：5
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