Socio-cognitive caste-based optimization

Urbańczyk，Aleksandra1; Kipiński，Piotr1; Nabywaniec，Mateusz1; Rutkowski，Leszek1; Chong，Siang Yew2; Yao，Xin2; Boryczko，Krzysztof1; Byrski，Aleksander1

2023-09-01

10.1016/j.jocs.2023.102098

Journal of Computational Science   Impact Factor & Quartile

1877-7503

1877-7511

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.

Global optimization Metaheuristics Socio-cognitive computing

SCI

English

Others

Polish National Science Center[2019/35/O/ST6/00570] ; ARTIQ project - NCN["DEC-2021/01/2/ST6/00004","NCBR DWP/ARTIQ-I/426/2023"]

Computer Science

Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods

WOS:001040061600001

ELSEVIER

2-s2.0-85166654967

Scopus

1.AGH University of Science and Technology,Krakow,Al. Mickiewicza 30,30-010,Poland
2.Southern University of Science and Technology (SUSTech),Shenzhen,China

Urbańczyk，Aleksandra,Kipiński，Piotr,Nabywaniec，Mateusz,et al. Socio-cognitive caste-based optimization[J]. Journal of Computational Science,2023,72.

Urbańczyk，Aleksandra.,Kipiński，Piotr.,Nabywaniec，Mateusz.,Rutkowski，Leszek.,Chong，Siang Yew.,...&Byrski，Aleksander.(2023).Socio-cognitive caste-based optimization.Journal of Computational Science,72.

Urbańczyk，Aleksandra,et al."Socio-cognitive caste-based optimization".Journal of Computational Science 72(2023).