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

Peng Chen, Shihua Zhou, Qiang Zhang, Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
57 Downloads (Pure)


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.

Original languageEnglish
Article number104805
Pages (from-to)1-12
Number of pages12
JournalEngineering Applications of Artificial Intelligence
Early online date18 Mar 2022
Publication statusPublished (in print/issue) - 31 May 2022

Bibliographical note

Funding Information:
This work is supported by the National Key R & D Program of China (No. 2018YFC0910 500 ), the National Natural Science Foundation of China (Nos. 61425002 , 61751203 , 61772100 , 61972266 , 61802040 , 61672121 , 61572093 ), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R07 ), the Program for Liaoning Innovative Research Team in University (No. LT2017012 ), the Natural Science Foundation of Liaoning Province (Nos. 20180551241 , 2019-ZD0567 ), the High-level Talent Innovation Support Program of Dalian City (Nos. 2017RQ060 , 2018RQ75 ), the Dalian Outstanding Young Science and Technology Talent Support Program (No. 2017RJ08 ), Scientific Research Fund of Liaoning Provincial Education Department, China (No. LJKZ1186 , JYT19051 ), Dalian University Scientific Research Platform Program (No. 202101YB02 ).

Publisher Copyright:
© 2022 Elsevier Ltd


  • Termite queen algorithm
  • Benchmarked functions
  • Loss minimization
  • Metaheuristic technique
  • Engineering design problems


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