Social spider optimization algorithm: survey and new applications
The behavior of insects and animals has inspired particle swarm optimization (PSO). An insect’s settlement acts as an integrated part that works as a speeded unit, also doing construction for huge projects. Besides the connections between insect societies, they are communicated internally between th...
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| Other Authors: | , , , , , , |
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2024
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1613 |
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| _version_ | 1857415063950852097 |
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| author | Abualigah, Laith |
| author2 | Al Turk, Ahmad A. Ikotun, Abiodun M. Abu Zitar, Raed Alsoud, Anas Ratib Khodadadi, Nima Hussien, Abdelazim G. Jia, Heming |
| author2_role | author author author author author author author |
| author_facet | Abualigah, Laith Al Turk, Ahmad A. Ikotun, Abiodun M. Abu Zitar, Raed Alsoud, Anas Ratib Khodadadi, Nima Hussien, Abdelazim G. Jia, Heming |
| author_role | author |
| dc.creator.none.fl_str_mv | Abualigah, Laith Al Turk, Ahmad A. Ikotun, Abiodun M. Abu Zitar, Raed Alsoud, Anas Ratib Khodadadi, Nima Hussien, Abdelazim G. Jia, Heming |
| dc.date.none.fl_str_mv | 2024-05-28T05:31:45Z 2024-05-28T05:31:45Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 9780443139253 https://depot.sorbonne.ae/handle/20.500.12458/1613 10.1016/B978-0-443-13925-3.00011-X |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Metaheuristic Optimization Algorithms Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications 978-0-443-13925-3 |
| dc.subject.none.fl_str_mv | Particle swarm optimization social spider optimization algorithm metaheuristic survey applications |
| dc.title.none.fl_str_mv | Social spider optimization algorithm: survey and new applications |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::book::book part |
| description | The behavior of insects and animals has inspired particle swarm optimization (PSO). An insect’s settlement acts as an integrated part that works as a speeded unit, also doing construction for huge projects. Besides the connections between insect societies, they are communicated internally between their members. Each spider has a weight based on the value of fitness. This algorithm consists of two search spiders called agents: males and females. This algorithm has been developed over time, resulting in many versions besides theories and findings. One of the PSO algorithms or versions is the social spider optimization (SSO) algorithm, a simulation of the interaction between spider groups, males and females. Based on gender, evolutionary factors simulate different behaviors usually found at their settlement based on the biological aspect. This survey studied the SSO and compared it with other PSO algorithms to find the best-performing algorithm based on a benchmark. This survey also studied the main applications of this algorithm in different fields, including medical, mathematical, artificial intelligence, engineering, and data engineering, and how this algorithm affected, impacted, and supported the different fields. Finally, this chapter provides an expectation of the fields that need to work with this algorithm to improve problem-solving and the fields that have a growing number studies that use this algorithm. |
| id | sorbonner_02fad8094ff2ace6b369f81445abd6ed |
| identifier_str_mv | 9780443139253 10.1016/B978-0-443-13925-3.00011-X |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1613 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Social spider optimization algorithm: survey and new applicationsAbualigah, LaithAl Turk, Ahmad A.Ikotun, Abiodun M.Abu Zitar, RaedAlsoud, Anas RatibKhodadadi, NimaHussien, Abdelazim G.Jia, HemingParticle swarm optimizationsocial spider optimizationalgorithmmetaheuristicsurveyapplicationsThe behavior of insects and animals has inspired particle swarm optimization (PSO). An insect’s settlement acts as an integrated part that works as a speeded unit, also doing construction for huge projects. Besides the connections between insect societies, they are communicated internally between their members. Each spider has a weight based on the value of fitness. This algorithm consists of two search spiders called agents: males and females. This algorithm has been developed over time, resulting in many versions besides theories and findings. One of the PSO algorithms or versions is the social spider optimization (SSO) algorithm, a simulation of the interaction between spider groups, males and females. Based on gender, evolutionary factors simulate different behaviors usually found at their settlement based on the biological aspect. This survey studied the SSO and compared it with other PSO algorithms to find the best-performing algorithm based on a benchmark. This survey also studied the main applications of this algorithm in different fields, including medical, mathematical, artificial intelligence, engineering, and data engineering, and how this algorithm affected, impacted, and supported the different fields. Finally, this chapter provides an expectation of the fields that need to work with this algorithm to improve problem-solving and the fields that have a growing number studies that use this algorithm.2024-05-28T05:31:45Z2024-05-28T05:31:45Z2024Controlled Vocabulary for Resource Type Genres::text::book::book partapplication/pdf9780443139253https://depot.sorbonne.ae/handle/20.500.12458/161310.1016/B978-0-443-13925-3.00011-XenMetaheuristic Optimization AlgorithmsMetaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications978-0-443-13925-3oai:depot.sorbonne.ae:20.500.12458/16132024-07-17T18:00:29Z |
| spellingShingle | Social spider optimization algorithm: survey and new applications Abualigah, Laith Particle swarm optimization social spider optimization algorithm metaheuristic survey applications |
| title | Social spider optimization algorithm: survey and new applications |
| title_full | Social spider optimization algorithm: survey and new applications |
| title_fullStr | Social spider optimization algorithm: survey and new applications |
| title_full_unstemmed | Social spider optimization algorithm: survey and new applications |
| title_short | Social spider optimization algorithm: survey and new applications |
| title_sort | Social spider optimization algorithm: survey and new applications |
| topic | Particle swarm optimization social spider optimization algorithm metaheuristic survey applications |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1613 |