Recent Versions and Applications of Sparrow Search Algorithm

被引:39
作者
Awadallah, Mohammed A. [1 ,2 ]
Al-Betar, Mohammed Azmi [3 ,4 ]
Doush, Iyad Abu [5 ,6 ]
Makhadmeh, Sharif Naser [3 ]
Al-Naymat, Ghazi [3 ]
机构
[1] Al Aqsa Univ, Dept Comp Sci, POB 4051, Gaza, Palestine
[2] Ajman Univ, Artificial Intelligence Res Ctr AIRC, Ajman, U Arab Emirates
[3] Ajman Univ, Coll Engn & Informat Technol, Artificial Intelligence Res Ctr AIRC, Ajman, U Arab Emirates
[4] Al Huson Univ Coll, Al Balqa Appl Univ, Dept Informat Technol, Irbid, Jordan
[5] Amer Univ Kuwait, Coll Engn & Appl Sci, Comp Dept, Salmiya, Kuwait
[6] Yarmouk Univ, Comp Sci Dept, Irbid, Jordan
关键词
NEURAL-NETWORK; OPTIMIZATION; PREDICTION; WIND;
D O I
10.1007/s11831-023-09887-z
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.
引用
收藏
页码:2831 / 2858
页数:28
相关论文
共 128 条
  • [51] Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm
    Liu, Tingting
    Yuan, Zhi
    Wu, Li
    Badami, Benjamin
    [J]. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2021, 31 (04) : 1921 - 1935
  • [52] Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings
    Lv, Jie
    Sun, Wenlei
    Wang, Hongwei
    Zhang, Fan
    [J]. SENSORS, 2021, 21 (16)
  • [53] [吕鑫 Lyu Xin], 2021, [北京航空航天大学学报, Journal of Beijing University of Aeronautics and Astronautics], V47, P1712
  • [54] Multi-threshold image segmentation based on improved sparrow search algorithm
    Lyu X.
    Mu X.
    Zhang J.
    [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2021, 43 (02): : 318 - 327
  • [55] Enhanced Sparrow Search Algorithm With Mutation Strategy for Global Optimization
    Ma, Bing
    Lu, Pengmin
    Zhang, Lufan
    Liu, Yonggang
    Zhou, Qiang
    Chen, Yixin
    Qi, Qisong
    Hu, Yongtao
    [J]. IEEE ACCESS, 2021, 9 : 159218 - 159261
  • [56] Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems
    Ma, Jie
    Hao, Zhiyuan
    Sun, Wenjing
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2022, 59 (02)
  • [57] [马卫 Ma Wei], 2022, [应用科学学报, Journal of Applied Sciences], V40, P116
  • [58] Malicious URL Classification Model Based on Improved Sparrow Search Algorithm
    Ma, Yiran
    Guan, Qihang
    Guo, Fengyuan
    Zhang, Guidong
    [J]. PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021), 2021, : 21 - 25
  • [59] Multi-objective power scheduling problem in smart homes using grey wolf optimiser
    Makhadmeh, Sharif Naser
    Khader, Ahamad Tajudin
    Al-Betar, Mohammed Azmi
    Naim, Syibrah
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (09) : 3643 - 3667
  • [60] Opposition based learning: A literature review
    Mandavi, Sedigheh
    Rahnamayan, Shahryar
    Deb, Kalyanmoy
    [J]. SWARM AND EVOLUTIONARY COMPUTATION, 2018, 39 : 1 - 23