Socio-cognitive caste-based optimization

Aleksandra URBAŃCZYK, Piotr KIPIŃSKI, Mateusz NABYWANIEC, Leszek RUTKOWSKI, Siang Yew CHONG, Xin YAO, Krzysztof BORYCZKO, Aleksander BYRSKI

Research output: Journal PublicationsJournal Article (refereed)peer-review


Metaheuristics are universal optimization algorithms that are used to solve difficult problems, which are unsolvable by classic approaches. In this paper, we aim to construct a novel class of socio-cognitive metaheuristics based on the caste metaphor. We focus on classic evolutionary and agent-based metaheuristics, adding a sociologically inspired structure of the population and cognitively inspired variation operators. In addition to giving the background and details of the proposed algorithms, we apply them to the optimization of a variety of difficult benchmark problems. © 2023 The Author(s)
Original languageEnglish
Article number102098
JournalJournal of Computational Science
Early online date4 Jul 2023
Publication statusPublished - Sept 2023
Externally publishedYes

Bibliographical note

The research presented in this paper has been financially supported by: Polish National Science Center Grant no. 2019/35/O/ST6/00570 “Socio-cognitive inspirations in classic metaheuristics”. (AU), Polish Ministry of Science and Higher Education funds assigned to AGH University of Science and Technology (KB) and ARTIQ project – NCN: DEC-2021/01/2/ST6/00004 , NCBR DWP/ARTIQ-I/426/2023 (AB) .


  • Global optimization
  • Metaheuristics
  • Socio-cognitive computing


Dive into the research topics of 'Socio-cognitive caste-based optimization'. Together they form a unique fingerprint.

Cite this