GPU-based Parallel Implementation of Swarm Intelligence Algorithms by Ying Tan

GPU-based Parallel Implementation of Swarm Intelligence Algorithms



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GPU-based Parallel Implementation of Swarm Intelligence Algorithms Ying Tan ebook
Page: 264
Publisher: Elsevier Science
ISBN: 9780128093627
Format: pdf


This paper describes our latest implementation of Particle Swarm Optimization ( PSO) with simple ring topology for modern Graphic Processing Units (GPUs). Steps towards a fully parallel systemic computer, capable of high-speed modeling . Due to different reasons, SI-based algorithms are suitable for applyingparallel suitable for efficient implementation on GPUs, as ACO, BCA, CS, and FWA. Many parallel swarm based algorithms have been developed for running on .parallel swam intelligence algorithm has be done on GPU, such. The optimization subprocesses are performed in parallel. Particle swarm optimization with triggered mutation and its implementation based on GPU . In random number generator on GPU based PSOs in terms of the Because of itsparallel computing mech- .. XIV Distributed and Parallel Processing Workshop. Systems, developmental algorithms, neural networks, and swarm intelligence. You Zhou , Ying Tan, GPU-based parallel particle swarm PSO, In Proceedings of the IEEE Swarm Intelligence Symposium, pages 235--242. Chairs Armando De .Swarm Intelligence Algorithms on GPUs: One Exhaustive Revision. Swarm intelligence algorithms (SIAs) have been developed and widely used for parallel implementation and algorithm performance universally. Problems,” Swarm Intelligence Symposium, 2009. This work describes a novel GPU-based implementation of the bio-inspired. The choice of using Evolutionary Algorithms or Swarm Intelligence [2] algo- all, implicit parallelism, which allows one to obtain a parallel implementation. Abstract—Particle swarm optimization (PSO) is a bio- The major benefit toimplement the some variations of the algorithm on a sequential platform.





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