A meta-inspired termite queen algorithm for global optimization and engineering design problems

被引:21
|
作者
Chen, Peng [1 ]
Zhou, Shihua [1 ]
Zhang, Qiang [1 ,2 ]
Kasabov, Nikola [3 ,4 ]
机构
[1] Dalian Univ, Key Lab Adv Design & Intelligent Comp, Minist Educ, Dalian 116622, Peoples R China
[2] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
[3] Auckland Univ Technol, Knowledge Engn & Discovery Res Inst, Auckland 1010, New Zealand
[4] Ulster Univ, Intelligent Syst Res Ctr, Coleraine BT52 1SA, North Ireland
关键词
Termite queen algorithm; Benchmarked functions; Loss minimization; Metaheuristic technique; Engineering design problems; DIFFERENTIAL EVOLUTION; SEARCH;
D O I
10.1016/j.engappai.2022.104805
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen's rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems
    Braik, Malik
    Hammouri, Abdelaziz
    Atwan, Jaffar
    Al-Betar, Mohammed Azmi A.
    Awadallah, Mohammed A.
    KNOWLEDGE-BASED SYSTEMS, 2022, 243
  • [32] A MODIFIED FIREFLY ALGORITHM FOR ENGINEERING DESIGN OPTIMIZATION PROBLEMS
    Kazemzadeh-Parsi, M. J.
    IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF MECHANICAL ENGINEERING, 2014, 38 (M2) : 403 - 421
  • [33] Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
    Wang, Gai-Ge
    MEMETIC COMPUTING, 2018, 10 (02) : 151 - 164
  • [34] Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
    Gai-Ge Wang
    Memetic Computing, 2018, 10 : 151 - 164
  • [35] Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems
    Jiang, Yuxin
    Wu, Qing
    Zhu, Shenke
    Zhang, Luke
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 188
  • [36] Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems
    Zhu, Donglin
    Wang, Siwei
    Zhou, Changjun
    Yan, Shaoqiang
    Xue, Jiankai
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 237
  • [37] African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems
    Abdollahzadeh, Benyamin
    Gharehchopogh, Farhad Soleimanian
    Mirjalili, Seyedali
    COMPUTERS & INDUSTRIAL ENGINEERING, 2021, 158
  • [38] A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems
    Zhang, Ziyu
    Gao, Yuelin
    Zuo, Wenlu
    IEEE Access, 2022, 10 : 55988 - 56016
  • [39] Ebola Optimization Search Algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems
    Oyelade, Olaide N.
    Ezugwu, Absalom E.
    INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND ENERGY TECHNOLOGIES (ICECET 2021), 2021, : 1041 - 1050
  • [40] A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems
    Zhang, Ziyu
    Gao, Yuelin
    Zuo, Wenlu
    IEEE ACCESS, 2022, 10 : 55988 - 56016