Socio-cognitive caste-based optimization

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

doi:10.1016/j.jocs.2023.102098

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 Publications › Journal Article (refereed) › peer-review

Abstract

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.

Original language	English
Article number	102098
Journal	Journal of Computational Science
Volume	72
Early online date	4 Jul 2023
DOIs	https://doi.org/10.1016/j.jocs.2023.102098
Publication status	Published - Sept 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Funding

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).

Keywords

Global optimization
Metaheuristics
Socio-cognitive computing

Access to Document

10.1016/j.jocs.2023.102098

Cite this

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title = "Socio-cognitive caste-based optimization",

abstract = "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.",

keywords = "Global optimization, Metaheuristics, Socio-cognitive computing",

author = "Aleksandra URBA{\'N}CZYK and Piotr KIPI{\'N}SKI and Mateusz NABYWANIEC and Leszek RUTKOWSKI and CHONG, \{Siang Yew\} and Xin YAO and Krzysztof BORYCZKO and Aleksander BYRSKI",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = sep,

doi = "10.1016/j.jocs.2023.102098",

language = "English",

volume = "72",

journal = "Journal of Computational Science",

issn = "1877-7503",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Socio-cognitive caste-based optimization

AU - URBAŃCZYK, Aleksandra

AU - KIPIŃSKI, Piotr

AU - NABYWANIEC, Mateusz

AU - RUTKOWSKI, Leszek

AU - CHONG, Siang Yew

AU - YAO, Xin

AU - BORYCZKO, Krzysztof

AU - BYRSKI, Aleksander

PY - 2023/9

Y1 - 2023/9

N2 - 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.

AB - 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.

KW - Global optimization

KW - Metaheuristics

KW - Socio-cognitive computing

UR - https://www.scopus.com/pages/publications/85166654967

U2 - 10.1016/j.jocs.2023.102098

DO - 10.1016/j.jocs.2023.102098

M3 - Journal Article (refereed)

SN - 1877-7503

VL - 72

JO - Journal of Computational Science

JF - Journal of Computational Science

M1 - 102098

ER -

Socio-cognitive caste-based optimization

Abstract

Bibliographical note

Funding

Keywords

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