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Centripetal Accelerated Particle Swarm Optimization And Applications
Details
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.
Autorentext
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.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783639707076
- Sprache Englisch
- Größe H220mm x B150mm x T12mm
- Jahr 2014
- EAN 9783639707076
- Format Kartonierter Einband
- ISBN 3639707079
- Veröffentlichung 09.04.2014
- Titel Centripetal Accelerated Particle Swarm Optimization And Applications
- Autor Zahra Beheshti , Siti Mariyam Shamsuddin
- Untertitel CAPSO and its Applications in Machine Learning
- Gewicht 310g
- Herausgeber Scholars' Press
- Anzahl Seiten 196
- Genre Wirtschaft