APSM-jS']jSO: A novel jS']jSO variant with an adaptive parameter selection mechanism and a new external archive updating mechanism

被引:29
作者
Li, Yintong [1 ]
Han, Tong [1 ]
Zhou, Huan [1 ]
Wei, Yujie [2 ,3 ]
Wang, Yuan [1 ]
Tan, Mulai [1 ]
Huang, Changqiang [1 ]
机构
[1] Air Force Engn Univ, Aviat Engn Sch, Xian 710038, Peoples R China
[2] Air Force Engn Univ, Air Def & Antimissile Sch, Xian 710038, Peoples R China
[3] Air Force Xian Flying Coll, Xian 710300, Peoples R China
基金
中国国家自然科学基金;
关键词
Evolutionary computation; Differential evolution; Global optimization; IEEE CEC 2018 test suite; DIFFERENTIAL EVOLUTION ALGORITHM; L-SHADE; ENSEMBLE; OPTIMIZATION; ADAPTATION; HYBRID; LSHADE;
D O I
10.1016/j.swevo.2023.101283
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel jSO variant named APSM-jSO is proposed in this study by making simple and effective modifications to improve its performance. There are three main differences between APSM-jSO and jSO. First, a novel adaptive selection mechanism (APSM) for selecting entries from the historical memory is designed to fully utilize the better entries in the historical memory. Subsequently, the first in-first out (FIFO) method is utilized to update the external archive for maintaining the population diversity and avoiding overuse of the external archive. Finally, a new mutation strategy adopting rank-based selective pressure (RSP) is used to enhance the exploitation of APSM-jSO. APSM-jSO is evaluated using the IEEE CEC 2018 test suite in comparison with five state-of-the-art DE-based variants (ELSHADE-SPACMA, EB-LSHADE, LSHADE-RSP, mL-SHADE, and MadDE) and five winners of the IEEE CEC competitions (AGSK, APGSK-IMODE, EBOwithCMAR, HSES, and IMODE). The results demonstrate that APSM-jSO outperforms jSO and five DE-based algorithms, is superior to four top winners of the IEEE CEC competitions (AGSK, APGSK-IMODE, HSES, and IMODE), and is not inferior to the top method of the IEEE CEC 2017 competition (EBOwithCMAR). The conclusion is that APSM-jSO effectively further enhances the overall performance of jSO, and is an excellent jSO variant. The MATLAB source code of APSM-jSO can bedownloaded from https://github.com/Yintong-Li/APSM-jSO.
引用
收藏
页数:18
相关论文
共 46 条
[41]   Ensemble of differential evolution variants [J].
Wu, Guohua ;
Shen, Xin ;
Li, Haifeng ;
Chen, Huangke ;
Lin, Anping ;
Suganthan, P. N. .
INFORMATION SCIENCES, 2018, 423 :172-186
[42]  
Yeh JF, 2019, IEEE C EVOL COMPUTAT, P381, DOI [10.1109/CEC.2019.8789991, 10.1109/cec.2019.8789991]
[43]   Adaptive differential evolution with ensembling operators for continuous optimization problems [J].
Yi, Wenchao ;
Chen, Yong ;
Pei, Zhi ;
Lu, Jiansha .
SWARM AND EVOLUTIONARY COMPUTATION, 2022, 69
[44]   Hybrid Sampling Evolution Strategy for Solving Single Objective Bound Constrained Problems [J].
Zhang, Geng ;
Shi, Yuhui .
2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2018, :765-771
[45]   JADE: Adaptive Differential Evolution With Optional External Archive [J].
Zhang, Jingqiao ;
Sanderson, Arthur C. .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (05) :945-958
[46]   A collaborative LSHADE algorithm with comprehensive learning mechanism [J].
Zhao, Fuqing ;
Zhao, Lexi ;
Wang, Ling ;
Song, Houbin .
APPLIED SOFT COMPUTING, 2020, 96