Centripetal Accelerated Particle Swarm by Beheshti Zahra (5 results)

Paperback. Condition: Like New. Like New. book.

Condition: 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.

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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.

Taschenbuch. Condition: 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.