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Socio-cognitive caste-based optimization
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DOI：10.1016/j.jocs.2023.102098.png)
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Abstract
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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:
Socio-cognitive computing
Metaheuristics
Global optimization
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