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Sinopsis
Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton’s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces.
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Reseña del editor
Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton's laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces.
Biografía del autor
Dr. Zahra Beheshti received her BSc and MSc in Computer Engineering from Islamic Azad University Najafabad branch (IAUN), Iran and PhD in Artificial Intelligence from Universiti Teknologi Malaysia (UTM), Malaysia. Her current research interests include Artificial Intelligence and Soft Computing.
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Centripetal Accelerated Particle Swarm Optimization And Applications
Impresión bajo demandaLibrería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Beheshti ZahraDr. Zahra Beheshti received her BSc and MSc in Computer Engineering from Islamic Azad University Najafabad branch (IAUN), Iran and PhD in Artificial Intelligence from Universiti Teknologi Malaysia (UTM), Malaysia. Her c. Nº de ref. del artículo: 4999047
Cantidad disponible: Más de 20 disponibles
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Centripetal Accelerated Particle Swarm Optimization And Applications
Publicado por
Scholars' Press Apr 2014, 2014
ISBN 10: 3639707079
ISBN 13: 9783639707076
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Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. 196 pp. Englisch. Nº de ref. del artículo: 9783639707076
Cantidad disponible: 2 disponibles
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Centripetal Accelerated Particle Swarm Optimization And Applications : CAPSO and its Applications in Machine Learning
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. Nº de ref. del artículo: 9783639707076
Cantidad disponible: 1 disponibles
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Centripetal Accelerated Particle Swarm Optimization And Applications
Publicado por
Scholars' Press Apr 2014, 2014
ISBN 10: 3639707079
ISBN 13: 9783639707076
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nowadays, meta-heuristic optimization algorithms have been extensively applied to a variety of Machine Learning (ML). The majority of them imitate the behavior of natural phenomena to find the best solution. The algorithms find promising regions in an affordable time because of exploration and exploitation ability. Although the mentioned algorithms have satisfactory results in various fields, none of them is able to present a higher performance for all applications. Therefore, searching for a new meta-heuristic algorithm is an open problem. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton¿s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO), is introduced. CAPSO accelerates the learning and convergence of ML problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch. Nº de ref. del artículo: 9783639707076
Cantidad disponible: 1 disponibles
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Centripetal Accelerated Particle Swarm Optimization And Applications: CAPSO and its Applications in Machine Learning
Publicado por
Scholars* Press, 2014
ISBN 10: 3639707079
ISBN 13: 9783639707076
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Paperback
Librería: Mispah books, Redhill, SURRE, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA75836397070795
Cantidad disponible: 1 disponibles