A Comprehensive Review of Swarm Optimization Algorithms

Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known...

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Vydáno v:	PloS one Ročník 10; číslo 5; s. e0122827
Hlavní autoři:	Ab Wahab, Mohd Nadhir, Nefti-Meziani, Samia, Atyabi, Adham
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 18.05.2015 Public Library of Science (PLoS)
Témata:	Algorithms Analysis Animals Ants - physiology Applied mathematics Bees - physiology Behavior, Animal Biogeography Biological Evolution Birds - physiology Chromosomes Comparative analysis Computer engineering Computer science Computer Simulation Elitism Evolutionary algorithms Genetic algorithms International conferences Models, Biological Mutation Optimization Optimization algorithms Optimization theory Particle swarm optimization Performance evaluation Population Robotics Selection, Genetic Social Behavior Statistical analysis Statistical tests Studies Swarm intelligence Systems Biology Australia
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
AbstractList	Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Audience	Academic
Author	Nefti-Meziani, Samia Atyabi, Adham Ab Wahab, Mohd Nadhir
AuthorAffiliation	Politehnica University of Bucharest, ROMANIA 2 School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, Adelaide, Australia 1 Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom
AuthorAffiliation_xml	– name: 2 School of Computer Science, Engineering and Mathematics, Flinders University of South Australia, Adelaide, Australia – name: 1 Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom – name: Politehnica University of Bucharest, ROMANIA
Author_xml	– sequence: 1 givenname: Mohd Nadhir surname: Ab Wahab fullname: Ab Wahab, Mohd Nadhir – sequence: 2 givenname: Samia surname: Nefti-Meziani fullname: Nefti-Meziani, Samia – sequence: 3 givenname: Adham surname: Atyabi fullname: Atyabi, Adham
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25992655$$D View this record in MEDLINE/PubMed
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: MNAW AA. Performed the experiments: MNAW AA. Analyzed the data: AA. Contributed reagents/materials/analysis tools: MNAW. Wrote the paper: MNAW SNM AA.
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Snippet	Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these... Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these...
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SubjectTerms	Algorithms Analysis Animals Ants - physiology Applied mathematics Bees - physiology Behavior, Animal Biogeography Biological Evolution Birds - physiology Chromosomes Comparative analysis Computer engineering Computer science Computer Simulation Elitism Evolutionary algorithms Genetic algorithms International conferences Models, Biological Mutation Optimization Optimization algorithms Optimization theory Particle swarm optimization Performance evaluation Population Robotics Selection, Genetic Social Behavior Statistical analysis Statistical tests Studies Swarm intelligence Systems Biology
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Title	A Comprehensive Review of Swarm Optimization Algorithms
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