GPU-parallelized artificial bee colony algorithm (GP-ABC) in data clustering application
Artificial Bee Colony (ABC) optimization and k-means algorithm are popularly used in data clustering application due to their accuracy and simplicity. However, as the number of dimension and data increases, program complexity may increase much further and ABC will execute in much slower time. This p...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2014
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| Online Access: | http://hdl.handle.net/10356/59878 |
| Summary: | Artificial Bee Colony (ABC) optimization and k-means algorithm are popularly used in data clustering application due to their accuracy and simplicity. However, as the number of dimension and data increases, program complexity may increase much further and ABC will execute in much slower time. This project proposes a novel parallelization model on ABC called ‘GPU-parallelized Artificial Bee Colony (GP-ABC)’ algorithm in order to achieve speedup relatively to its normal sequential program execution. Testing has been done on several datasets from UCI Machine Learning repository such as Iris and Wine datasets. The results were encouraging and outperformed the ordinary ABC algorithm in terms of processing time. |
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