An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

Existing bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the soc...

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Autores principales:	Samera Uga Otor, Bodunde Odunola Akinyemi, Temitope Adegboye Aladesanmi, Ganiyu Adesola Aderounmu, B.H. Kamagaté
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	bio-inspired optimisation social spider self-evolving Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/e754879ebee242f98a87048ddbf9e724
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spelling	oai:doaj.org-article:e754879ebee242f98a87048ddbf9e7242021-11-04T15:51:55ZAn adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider2331-191610.1080/23311916.2019.1588681https://doaj.org/article/e754879ebee242f98a87048ddbf9e7242019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311916.2019.1588681https://doaj.org/toc/2331-1916Existing bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern was controlled by the position of the prey for convergence. The proposed model was tested for convergence using several benchmark functions with different characteristics to evaluate its performance and results compared to an existing state of the arts’ spider algorithm. Results showed that the proposed model performed better by searching the optimum solution of the benchmark functions used to test the model.Samera Uga OtorBodunde Odunola AkinyemiTemitope Adegboye AladesanmiGaniyu Adesola AderounmuB.H. KamagatéTaylor & Francis Grouparticlebio-inspiredoptimisationsocial spiderself-evolvingEngineering (General). Civil engineering (General)TA1-2040ENCogent Engineering, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	bio-inspired optimisation social spider self-evolving Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	bio-inspired optimisation social spider self-evolving Engineering (General). Civil engineering (General) TA1-2040 Samera Uga Otor Bodunde Odunola Akinyemi Temitope Adegboye Aladesanmi Ganiyu Adesola Aderounmu B.H. Kamagaté An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
description	Existing bio-inspired models are challenged with premature convergence among others. In this paper, an adaptive social spider colony optimisation model based on the foraging behaviour of social spider was proposed as an optimisation problem. The algorithm mimics the prey capture behaviour of the social spider in which, the spider senses the presence of the prey through vibrations transmitted along the web thread. Spiders are the search agents while the web is the search space of the optimisation problem. The natural or biological phenomenon of vibration was modeled using wave theory while optimisation theory was considered in optimizing the objective function of the optimisation problem. This objective function was considered to be the frequency of vibration of the spiders and the prey as this is the function that enables the spider differentiates the vibration of the prey from that of neighbouring spiders and therefore forages maximally. To address the parameter tuning problem, the search pattern was controlled by the position of the prey for convergence. The proposed model was tested for convergence using several benchmark functions with different characteristics to evaluate its performance and results compared to an existing state of the arts’ spider algorithm. Results showed that the proposed model performed better by searching the optimum solution of the benchmark functions used to test the model.
format	article
author	Samera Uga Otor Bodunde Odunola Akinyemi Temitope Adegboye Aladesanmi Ganiyu Adesola Aderounmu B.H. Kamagaté
author_facet	Samera Uga Otor Bodunde Odunola Akinyemi Temitope Adegboye Aladesanmi Ganiyu Adesola Aderounmu B.H. Kamagaté
author_sort	Samera Uga Otor
title	An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
title_short	An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
title_full	An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
title_fullStr	An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
title_full_unstemmed	An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
title_sort	adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/e754879ebee242f98a87048ddbf9e724
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An adaptive bio-inspired optimisation model based on the foraging behaviour of a social spider

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