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 Publication › Article › 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
DOIs	https://doi.org/10.1016/j.jocs.2023.102098
Publication status	Published - Sept 2023
Externally published	Yes

Free Keywords

Global optimization
Metaheuristics
Socio-cognitive computing

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science
Modelling and Simulation

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10.1016/j.jocs.2023.102098

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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",

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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 - Article

AN - SCOPUS:85166654967

SN - 1877-7503

VL - 72

JO - Journal of Computational Science

JF - Journal of Computational Science

M1 - 102098

ER -

Socio-cognitive caste-based optimization

Abstract

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ASJC Scopus subject areas

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