Handbook of research on fireworks algorithms and swarm intelligence:
""This book provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniq...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, PA
IGI Global
[2020]
|
| Schlagworte: | |
| Online-Zugang: | DE-1050 DE-706 DE-83 DE-898 URL des Erstveröffentlichers |
| Zusammenfassung: | ""This book provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm"--Provided by publisher"-- |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781799816607 |
| DOI: | 10.4018/978-1-7998-1659-1 |
Internformat
MARC
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| 245 | 1 | 0 | |a Handbook of research on fireworks algorithms and swarm intelligence |c Ying Tan |
| 246 | 1 | 3 | |a Fireworks algorithms and swarm intelligence |
| 264 | 1 | |a Hershey, PA |b IGI Global |c [2020] | |
| 300 | |a 1 Online-Ressource | ||
| 336 | |b txt |2 rdacontent | ||
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| 505 | 8 | |a Section 1. Survey of FWA studies and applications. Chapter 1. Recent developments of fireworks algorithms -- Section 2. Algorithm improvements on FWA. Chapter 2. Last-position elimination-based fireworks algorithm for function optimization ; Chapter 3. Explosion operation of fireworks algorithm -- | |
| 505 | 8 | |a Section 3. FWA applications in machine learning. Chapter 4. EFWA as a method of optimizing model parameters: example of an expensive function evaluation ; Chapter 5. Learning from class imbalance: a fireworks-based resampling for weighted pattern matching classifier (PMC) ; Chapter 6. An improved dynamic search fireworks algorithm optimizes extreme learning machine to predict virtual machine fault ; Chapter 7. A classification model based on improved self-adaptive fireworks algorithm ; Chapter 8. Development and performance analysis of fireworks algorithm-trained artificial neural network (FWANN): a case study on financial time series forecasting ; Chapter 9. Interval type 2 fuzzy fireworks algorithm for clustering -- | |
| 505 | 8 | |a Section 4. FWA application in engineering. Chapter 10. A hybrid fireworks algorithm to navigation and mapping ; Chapter 11. Application of fireworks algorithm in bioinformatics ; Chapter 12. Increasing energy efficiency by optimizing the electrical infrastructure of a railway Line using fireworks algorithm ; Chapter 13. Hybrid bare bones fireworks algorithm for load flow analysis of islanded microgrids ; Chapter 14. A fireworks-based approach for efficient packet filtering in firewall ; Chapter 15. Innovative aspects of virtual reality and kinetic sensors for significant improvement using fireworks algorithm in a wii game of a collaborative sport -- | |
| 505 | 8 | |a Section 5. Innovative applications of swarm intelligence and swarm robotics. Chapter 16. Optimization of PID controller for a hybrid power system using particle swarm optimization technique ; Chapter 17. A survey on the applications of swarm intelligence to software verification ; Chapter 18. Research on the construction of underwater platform combat deduction system based on service-oriented and multi-agent technology | |
| 520 | |a ""This book provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm"--Provided by publisher"-- | ||
| 650 | 4 | |a Fireworks / Research | |
| 650 | 4 | |a Swarm intelligence | |
| 650 | 7 | |a Swarm intelligence |2 fast | |
| 700 | 1 | |a Tan, Ying |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, hardcover |z 978-1-7998-1659-1 |
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Datensatz im Suchindex
| _version_ | 1805076699002437632 |
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| any_adam_object | |
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| author2 | Tan, Ying |
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| contents | Section 1. Survey of FWA studies and applications. Chapter 1. Recent developments of fireworks algorithms -- Section 2. Algorithm improvements on FWA. Chapter 2. Last-position elimination-based fireworks algorithm for function optimization ; Chapter 3. Explosion operation of fireworks algorithm -- Section 3. FWA applications in machine learning. Chapter 4. EFWA as a method of optimizing model parameters: example of an expensive function evaluation ; Chapter 5. Learning from class imbalance: a fireworks-based resampling for weighted pattern matching classifier (PMC) ; Chapter 6. An improved dynamic search fireworks algorithm optimizes extreme learning machine to predict virtual machine fault ; Chapter 7. A classification model based on improved self-adaptive fireworks algorithm ; Chapter 8. Development and performance analysis of fireworks algorithm-trained artificial neural network (FWANN): a case study on financial time series forecasting ; Chapter 9. Interval type 2 fuzzy fireworks algorithm for clustering -- Section 4. FWA application in engineering. Chapter 10. A hybrid fireworks algorithm to navigation and mapping ; Chapter 11. Application of fireworks algorithm in bioinformatics ; Chapter 12. Increasing energy efficiency by optimizing the electrical infrastructure of a railway Line using fireworks algorithm ; Chapter 13. Hybrid bare bones fireworks algorithm for load flow analysis of islanded microgrids ; Chapter 14. A fireworks-based approach for efficient packet filtering in firewall ; Chapter 15. Innovative aspects of virtual reality and kinetic sensors for significant improvement using fireworks algorithm in a wii game of a collaborative sport -- Section 5. Innovative applications of swarm intelligence and swarm robotics. Chapter 16. Optimization of PID controller for a hybrid power system using particle swarm optimization technique ; Chapter 17. A survey on the applications of swarm intelligence to software verification ; Chapter 18. Research on the construction of underwater platform combat deduction system based on service-oriented and multi-agent technology |
| ctrlnum | (OCoLC)1153983031 (DE-599)BVBBV046701186 |
| doi_str_mv | 10.4018/978-1-7998-1659-1 |
| format | Electronic eBook |
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| id | DE-604.BV046701186 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:28:09Z |
| indexdate | 2024-07-20T06:01:26Z |
| institution | BVB |
| isbn | 9781799816607 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032111731 |
| oclc_num | 1153983031 |
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| publishDate | 2020 |
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| publisher | IGI Global |
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| spelling | Handbook of research on fireworks algorithms and swarm intelligence Ying Tan Fireworks algorithms and swarm intelligence Hershey, PA IGI Global [2020] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Section 1. Survey of FWA studies and applications. Chapter 1. Recent developments of fireworks algorithms -- Section 2. Algorithm improvements on FWA. Chapter 2. Last-position elimination-based fireworks algorithm for function optimization ; Chapter 3. Explosion operation of fireworks algorithm -- Section 3. FWA applications in machine learning. Chapter 4. EFWA as a method of optimizing model parameters: example of an expensive function evaluation ; Chapter 5. Learning from class imbalance: a fireworks-based resampling for weighted pattern matching classifier (PMC) ; Chapter 6. An improved dynamic search fireworks algorithm optimizes extreme learning machine to predict virtual machine fault ; Chapter 7. A classification model based on improved self-adaptive fireworks algorithm ; Chapter 8. Development and performance analysis of fireworks algorithm-trained artificial neural network (FWANN): a case study on financial time series forecasting ; Chapter 9. Interval type 2 fuzzy fireworks algorithm for clustering -- Section 4. FWA application in engineering. Chapter 10. A hybrid fireworks algorithm to navigation and mapping ; Chapter 11. Application of fireworks algorithm in bioinformatics ; Chapter 12. Increasing energy efficiency by optimizing the electrical infrastructure of a railway Line using fireworks algorithm ; Chapter 13. Hybrid bare bones fireworks algorithm for load flow analysis of islanded microgrids ; Chapter 14. A fireworks-based approach for efficient packet filtering in firewall ; Chapter 15. Innovative aspects of virtual reality and kinetic sensors for significant improvement using fireworks algorithm in a wii game of a collaborative sport -- Section 5. Innovative applications of swarm intelligence and swarm robotics. Chapter 16. Optimization of PID controller for a hybrid power system using particle swarm optimization technique ; Chapter 17. A survey on the applications of swarm intelligence to software verification ; Chapter 18. Research on the construction of underwater platform combat deduction system based on service-oriented and multi-agent technology ""This book provides vital research on theory analysis, improvements, and applications of fireworks algorithm. While highlighting topics such as convergence rate, parameter applications, and global optimization analysis, this publication explores up-to-date progress on the specific techniques of this algorithm"--Provided by publisher"-- Fireworks / Research Swarm intelligence Swarm intelligence fast Tan, Ying edt Erscheint auch als Druck-Ausgabe, hardcover 978-1-7998-1659-1 https://doi.org/10.4018/978-1-7998-1659-1 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Handbook of research on fireworks algorithms and swarm intelligence Section 1. Survey of FWA studies and applications. Chapter 1. Recent developments of fireworks algorithms -- Section 2. Algorithm improvements on FWA. Chapter 2. Last-position elimination-based fireworks algorithm for function optimization ; Chapter 3. Explosion operation of fireworks algorithm -- Section 3. FWA applications in machine learning. Chapter 4. EFWA as a method of optimizing model parameters: example of an expensive function evaluation ; Chapter 5. Learning from class imbalance: a fireworks-based resampling for weighted pattern matching classifier (PMC) ; Chapter 6. An improved dynamic search fireworks algorithm optimizes extreme learning machine to predict virtual machine fault ; Chapter 7. A classification model based on improved self-adaptive fireworks algorithm ; Chapter 8. Development and performance analysis of fireworks algorithm-trained artificial neural network (FWANN): a case study on financial time series forecasting ; Chapter 9. Interval type 2 fuzzy fireworks algorithm for clustering -- Section 4. FWA application in engineering. Chapter 10. A hybrid fireworks algorithm to navigation and mapping ; Chapter 11. Application of fireworks algorithm in bioinformatics ; Chapter 12. Increasing energy efficiency by optimizing the electrical infrastructure of a railway Line using fireworks algorithm ; Chapter 13. Hybrid bare bones fireworks algorithm for load flow analysis of islanded microgrids ; Chapter 14. A fireworks-based approach for efficient packet filtering in firewall ; Chapter 15. Innovative aspects of virtual reality and kinetic sensors for significant improvement using fireworks algorithm in a wii game of a collaborative sport -- Section 5. Innovative applications of swarm intelligence and swarm robotics. Chapter 16. Optimization of PID controller for a hybrid power system using particle swarm optimization technique ; Chapter 17. A survey on the applications of swarm intelligence to software verification ; Chapter 18. Research on the construction of underwater platform combat deduction system based on service-oriented and multi-agent technology Fireworks / Research Swarm intelligence Swarm intelligence fast |
| title | Handbook of research on fireworks algorithms and swarm intelligence |
| title_alt | Fireworks algorithms and swarm intelligence |
| title_auth | Handbook of research on fireworks algorithms and swarm intelligence |
| title_exact_search | Handbook of research on fireworks algorithms and swarm intelligence |
| title_exact_search_txtP | Handbook of research on fireworks algorithms and swarm intelligence |
| title_full | Handbook of research on fireworks algorithms and swarm intelligence Ying Tan |
| title_fullStr | Handbook of research on fireworks algorithms and swarm intelligence Ying Tan |
| title_full_unstemmed | Handbook of research on fireworks algorithms and swarm intelligence Ying Tan |
| title_short | Handbook of research on fireworks algorithms and swarm intelligence |
| title_sort | handbook of research on fireworks algorithms and swarm intelligence |
| topic | Fireworks / Research Swarm intelligence Swarm intelligence fast |
| topic_facet | Fireworks / Research Swarm intelligence |
| url | https://doi.org/10.4018/978-1-7998-1659-1 |
| work_keys_str_mv | AT tanying handbookofresearchonfireworksalgorithmsandswarmintelligence AT tanying fireworksalgorithmsandswarmintelligence |