Space–Time Concept in Social Network Search Algorithm

Talatahari, S; Bayzidi, H; Bayzidi, M

Space–Time Concept in Social Network Search Algorithm

Talatahari, S Bayzidi, H Bayzidi, M

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 212, pp. 197-227
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Talatahari, S
dc.contributor.author	Bayzidi, H
dc.contributor.author	Bayzidi, M
dc.date.accessioned	2023-07-09T21:57:32Z
dc.date.available	2023-07-09T21:57:32Z
dc.date.issued	2022-09-01
dc.identifier.citation	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 212, pp. 197-227
dc.identifier.isbn	978-3-031-07511-7
dc.identifier.uri	http://hdl.handle.net/10453/171382
dc.description.abstract	Nowadays, social networks have become popular platforms in which users can interact with each other virtually. The relationship among users, their influence on each other, and similarity in their behavior are the main features of users in the social networks. These features are utilized in the development of an optimization method, which is called the Social Network Search (SNS) algorithm. The SNS method is a recently developed optimizer, which models the treatment of users in expressing their new views. Four novel optimization operators with new formulations are invented, which are called decision moods. These moods are named imitation, conversation, disputation, and innovation, which are real-world behaviors of users in social networks and model how users are affected and motivated to share their new views. This chapter embeds the concept of space–time in the implementation process of the SNS algorithm to developing space–time social network search (STSNS) algorithm. Single-objective, bound-constraint benchmark problems of IEEE congress on evolutionary computation 2014 (CEC 2014) are utilized to study the efficiency of the STSNS algorithm in solving challenging optimization problems. In addition, the results of the STSNS method is compared with a variety range of optimization methods, including eight state-of-the-art, eight popular, and eight novel methods from the literature. Two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results demonstrate its superiority in dealing with most of the selected complex optimization problems.
dc.format.extent	10
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
dc.relation.ispartofseries	Studies in Systems, Decision and Control
dc.relation.isbasedon	10.1007/978-3-031-07512-4_7
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Space–Time Concept in Social Network Search Algorithm
dc.type	Chapter
utslib.citation.volume	212
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-07-09T21:57:31Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	212
dc.location	Switzerland

Abstract:

Nowadays, social networks have become popular platforms in which users can interact with each other virtually. The relationship among users, their influence on each other, and similarity in their behavior are the main features of users in the social networks. These features are utilized in the development of an optimization method, which is called the Social Network Search (SNS) algorithm. The SNS method is a recently developed optimizer, which models the treatment of users in expressing their new views. Four novel optimization operators with new formulations are invented, which are called decision moods. These moods are named imitation, conversation, disputation, and innovation, which are real-world behaviors of users in social networks and model how users are affected and motivated to share their new views. This chapter embeds the concept of space–time in the implementation process of the SNS algorithm to developing space–time social network search (STSNS) algorithm. Single-objective, bound-constraint benchmark problems of IEEE congress on evolutionary computation 2014 (CEC 2014) are utilized to study the efficiency of the STSNS algorithm in solving challenging optimization problems. In addition, the results of the STSNS method is compared with a variety range of optimization methods, including eight state-of-the-art, eight popular, and eight novel methods from the literature. Two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results demonstrate its superiority in dealing with most of the selected complex optimization problems.

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http://hdl.handle.net/10453/171382