Salp swarm algorithm: survey, analysis, and new applications
This chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. The behavior of the species when traveling and foraging in the waters is the main source...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1611 |
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| _version_ | 1857415063504158720 |
|---|---|
| author | Abualigah, Laith |
| author2 | Hawamdeh, Worod Abu Zitar, Raed AlZu’bi, Shadi Mughaid, Ala Hanandeh, Essam Said Alsoud, Anas Ratib El-kenawy, El-Sayed M. |
| author2_role | author author author author author author author |
| author_facet | Abualigah, Laith Hawamdeh, Worod Abu Zitar, Raed AlZu’bi, Shadi Mughaid, Ala Hanandeh, Essam Said Alsoud, Anas Ratib El-kenawy, El-Sayed M. |
| author_role | author |
| dc.creator.none.fl_str_mv | Abualigah, Laith Hawamdeh, Worod Abu Zitar, Raed AlZu’bi, Shadi Mughaid, Ala Hanandeh, Essam Said Alsoud, Anas Ratib El-kenawy, El-Sayed M. |
| dc.date.none.fl_str_mv | 2024-05-28T05:17:40Z 2024-05-28T05:17:40Z 2024 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 9780443139253 https://depot.sorbonne.ae/handle/20.500.12458/1611 10.1016/B978-0-443-13925-3.00009-1 |
| 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 | SALP swarm optimization multiobjective optimization algorithm genetic algorithm heuristic algorithm algorithm benchmark |
| dc.title.none.fl_str_mv | Salp swarm algorithm: survey, analysis, and new applications |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::book::book part |
| description | This chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. The results of the mathematical functions reveal that the SSA technique may improve the initial random solutions more effectively and efficiently. The findings of the MSSA method show that it can approach optimal Pareto solutions with strong convergence and coverage. The research also explains how to use SSA and MSSA to solve a number of computationally challenging and expensive engineering design issues (e.g., airfoil design and marine propeller design). The benefits of the proposed algorithms in addressing real-world issues with challenging and unknown search areas are demonstrated by the outcomes of real-world case studies. In this paper, the most important literature and previous studies related to the subject of the study were presented, where nearly 30 researches were referred to develop a theoretical framework related to SSA and other improved algorithms and to compare SSA with other systems. The MSSA approach has been linked to a large number of previously published algorithms. Many standard criteria that require individual and multiple objectives are included, and the most important findings of this study and the most important conclusions related to the subject of the study are included. |
| id | sorbonner_3c87e11169ebaa278410a16637b6fcf0 |
| identifier_str_mv | 9780443139253 10.1016/B978-0-443-13925-3.00009-1 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1611 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Salp swarm algorithm: survey, analysis, and new applicationsAbualigah, LaithHawamdeh, WorodAbu Zitar, RaedAlZu’bi, ShadiMughaid, AlaHanandeh, Essam SaidAlsoud, Anas RatibEl-kenawy, El-Sayed M.SALP swarm optimizationmultiobjective optimization algorithmgenetic algorithmheuristic algorithmalgorithmbenchmarkThis chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. The behavior of the species when traveling and foraging in the waters is the main source of SSA and MSSA. These two algorithms are put to test on a variety of mathematical optimization functions to see how they behave when it comes to finding the best solutions to optimization problems. The results of the mathematical functions reveal that the SSA technique may improve the initial random solutions more effectively and efficiently. The findings of the MSSA method show that it can approach optimal Pareto solutions with strong convergence and coverage. The research also explains how to use SSA and MSSA to solve a number of computationally challenging and expensive engineering design issues (e.g., airfoil design and marine propeller design). The benefits of the proposed algorithms in addressing real-world issues with challenging and unknown search areas are demonstrated by the outcomes of real-world case studies. In this paper, the most important literature and previous studies related to the subject of the study were presented, where nearly 30 researches were referred to develop a theoretical framework related to SSA and other improved algorithms and to compare SSA with other systems. The MSSA approach has been linked to a large number of previously published algorithms. Many standard criteria that require individual and multiple objectives are included, and the most important findings of this study and the most important conclusions related to the subject of the study are included.2024-05-28T05:17:40Z2024-05-28T05:17:40Z2024Controlled Vocabulary for Resource Type Genres::text::book::book partapplication/pdf9780443139253https://depot.sorbonne.ae/handle/20.500.12458/161110.1016/B978-0-443-13925-3.00009-1enMetaheuristic Optimization AlgorithmsMetaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications978-0-443-13925-3oai:depot.sorbonne.ae:20.500.12458/16112024-07-17T18:00:27Z |
| spellingShingle | Salp swarm algorithm: survey, analysis, and new applications Abualigah, Laith SALP swarm optimization multiobjective optimization algorithm genetic algorithm heuristic algorithm algorithm benchmark |
| title | Salp swarm algorithm: survey, analysis, and new applications |
| title_full | Salp swarm algorithm: survey, analysis, and new applications |
| title_fullStr | Salp swarm algorithm: survey, analysis, and new applications |
| title_full_unstemmed | Salp swarm algorithm: survey, analysis, and new applications |
| title_short | Salp swarm algorithm: survey, analysis, and new applications |
| title_sort | Salp swarm algorithm: survey, analysis, and new applications |
| topic | SALP swarm optimization multiobjective optimization algorithm genetic algorithm heuristic algorithm algorithm benchmark |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1611 |