Parallel Ant Colony Optimization algorithms for time series segmentation on a Multi-core Processor
This paper proposes four novel parallelization methods of a modified Ant Colony Optimization algorithm. The parallelization methods are aiming at finding the optimal segmentation scheme of time series with a low execution time. The series is decomposed into different sub-series firstly, and then each sub-series can be solved by colonies independently, finally merge the solutions of each colony to obtain the full segmentation scheme. According to the synchronization of individuals and colonies, we design four types of dual parallel models, and implement the parallel versions by using OpenMP library on a computing platform with a multicore processor for time series segmentation. Experiment results suggest that the parallel algorithms can greatly shorten the execution time without reducing the quality of the final solution.
time series Ant Colony Optimization parallelization segmentation multi-core
Huibin Liu Zhenfeng He
School of Mathematics and Computer Science Fuzhou University Fuzhou, China School of Mathematics and Computer Science Fuzhou Zniversity Fuzhou, China
国际会议
南昌
英文
340-343
2012-08-26（万方平台首次上网日期，不代表论文的发表时间）