We consider a metaheuristic optimization algorithm which uses single process (thread) to guide the search through the solution space. Thread performs in the cyclic way (iteratively) two main tasks: the goal function evaluation for a single solution or a set of solutions and management (solution filtering and selection, collection of history, updating). The latter task takes statistically 1-3% total iteration time, therefore we skip its acceleration as useless. The former task can be accelerated in parallel environments in various manners. We propose certain parallel small-grain calculation model providing the cost optimal method. Then, we carry out an experiment using Graphics Processing Unit (GPU) to confirm our theoretical results. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Bozejko, W., Smutnicki, C., & Uchroński, M. (2009). Parallel calculating of the goal function in metaheuristics using GPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5544 LNCS, pp. 1014–1023). https://doi.org/10.1007/978-3-642-01970-8_102
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