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Title: Parallelization of artificial immune systems using a massive parallel approach via modern GPUs (English)
Author: Khun, Jiří
Author: Šimeček, Ivan
Language: English
Journal: Programs and Algorithms of Numerical Mathematics
Volume: Proceedings of Seminar. Dolní Maxov, June 8-13, 2014
Issue: 2014
Pages: 106-111
Category: math
Summary: Parallelization is one of possible approaches for obtaining better results in terms of algorithm performance and overcome the limits of the sequential computation. In this paper, we present a study of parallelization of the opt-aiNet algorithm which comes from Artificial Immune Systems, one part of large family of population based algorithms inspired by nature. The opt-aiNet algorithm is based on an immune network theory which incorporates knowledge about mammalian immune systems in order to create a state-of-the-art algorithm suitable for the multimodal function optimization. The algorithm is known for a combination of local and global search with an emphasis on maintaining a stable set of distinct local extrema solutions. Moreover, its modifications can be used for many other purposes like data clustering or combinatorial optimization. The parallel version of the algorithm is designed especially for modern graphics processing units. The preliminary performance results show very significant speedup over the computation with traditional central processor units. (English)
Keyword: global optimization
Keyword: multimodal function
Keyword: parallelization
Keyword: opt-aiNet algorithm
Keyword: graphics processing unit
MSC: 65Y05
MSC: 68W10
MSC: 90C26
Date available: 2015-04-20T06:12:40Z
Last updated: 2023-06-05
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