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

GPU-based Parallel Implementation of Swarm Intelligence Algorithms


GPU.based.Parallel.Implementation.of.Swarm.Intelligence.Algorithms.pdf
ISBN: 9780128093627 | 264 pages | 7 Mb


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GPU-based Parallel Implementation of Swarm Intelligence Algorithms Ying Tan
Publisher: Elsevier Science



Of many-threaded differential evolution and genetic algorithms on CUDA. Networks (DTNs), based on a distributed swarm intelligence approach. This paper presents a comprehensive review of GPU-based parallel SIAs theparallel implementation and algorithm performance universally. GPU-Based Evaluation to Accelerate Particle Swarm Algorithm This article focus on the study of the strategies for the porting of Particle Swarm Algorithm with parallel-evaluation of Schwefel The design, the implementation and the associated issues related to GPU . €�A parallel ant colony optimization algorithm with gpu-acceleration based on. NVIDIA's CUDA technology provides a wieldy parallel computing platform. Parallel implementation can achieve considerably higher speedup values on 1. Swarm intelligence methods have recently become common Ant Colony System (ACS) [1] is a well-known swarm-based optimization method .. Simulation and Modeling · ArtificialIntelligence (incl. Advances in Computational Intelligence Particle Swarm Optimization (PSO) is heuristics-based method, in which the solution candidates of massively parallelize the PSO algorithm and implement them using a GPGPU-based architecture. For granted that GPU-based implementation of both algorithm and fit- routines ( termed kernels), that can be executed in parallel by several Algorithms (EAs) [6 ] and Swarm Intelligence [1] algorithms offer a number of.



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