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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| Vydané v: | PloS one Ročník 10; číslo 5; s. e0122827 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
Public Library of Science
18.05.2015
Public Library of Science (PLoS) |
| Predmet: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | 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. |
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| Bibliografia: | 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. |
| ISSN: | 1932-6203 1932-6203 |
| DOI: | 10.1371/journal.pone.0122827 |