Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

被引:0
作者
Eltaeib, Tarik [1 ]
Mahmood, Ausif [1 ]
机构
[1] Univ Bridgeport, Comp Sci & Engn, 126 Pk Ave, Bridgeport, CT 06605 USA
来源
COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2018, PT II | 2018年 / 11056卷
关键词
PARAMETERS;
D O I
10.1007/978-3-319-98446-9_5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis were also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm.
引用
收藏
页码:45 / 55
页数:11
相关论文
共 50 条
  • [21] Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems
    Vakhnin, Aleksei
    Sopov, Evgenii
    ALGORITHMS, 2021, 14 (05)
  • [22] Differential evolution algorithm with multi-population cooperation and multi-strategy integration
    Li, Xiaoyu
    Wang, Lei
    Jiang, Qiaoyong
    Li, Ning
    NEUROCOMPUTING, 2021, 421 : 285 - 302
  • [23] Differential evolution with the mutation strategy transformation based on a quartile for numerical optimization
    Jin, Peiyuan
    Cen, Jianming
    Feng, Quanxi
    Ai, Wu
    Chen, Huazhou
    Qiao, Hanli
    APPLIED INTELLIGENCE, 2024, 54 (01) : 334 - 356
  • [24] An improved multi-operator differential evolution via a knowledge-guided information sharing strategy for global optimization
    Yuan, Zhuoming
    Peng, Lei
    Dai, Guangming
    Wang, Maocai
    Zhang, Wanbing
    Zhou, Qingrui
    Zheng, Wei
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 269
  • [25] Using differential evolution to research the multi-objective optimization of medical sensor networks: a brief discussion
    Xu, Yulong
    Wang, Linjing
    Wang, Zhongyi
    Lv, Yali
    Zhang, Yanyun
    Chen, Wenwen
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 719 - 724
  • [26] On the Sensitivity of Large-Eddy Simulations of the Atmospheric Boundary Layer Coupled with Realistic Large-Scale Dynamics
    Giani, Paolo
    Crippa, Paola
    MONTHLY WEATHER REVIEW, 2024, 152 (04) : 1057 - 1075
  • [27] Adaptive Multi-subpopulation based Differential Evolution for Global Optimization
    Liu, Qingping
    Pang, Tingting
    Chen, Kaige
    Wang, Zuling
    Sheng, Weiguo
    2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2022,
  • [28] Broadband quasi perfect absorption using chirped multi-layer porous materials
    Jimenez, N.
    Romero-Garcia, V.
    Cebrecos, A.
    Pico, R.
    Sanchez-Morcillo, V. J.
    Garcia-Raffi, L. M.
    AIP ADVANCES, 2016, 6 (12):
  • [29] Optimization design the configuration sizes of multi-layer rectangle micro-channel heat sink
    Wang, Lifeng
    Shao, Baodong
    Cheng, Heming
    He, Ying
    ADVANCES IN COMPUTATIONAL MODELING AND SIMULATION, PTS 1 AND 2, 2014, 444-445 : 1101 - 1106
  • [30] A novel Two-layer Hierarchical Differential Evolution Algorithm for Global Optimization
    Zhou, Yinzhi
    Li, Xinyu
    Gao, Liang
    2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, : 2916 - 2921