GPU-based Parallel Implementation of Swarm Intelligence Algorithms by Ying Tan
GPU-based Parallel Implementation of Swarm Intelligence Algorithms Ying Tan ebook
Publisher: Elsevier Science
Optimization, Swarm Intelligence algorithms offer a number of attractive features: robust and . A new distributed particle swarm optimization algorithm for constraint reasoning. Particle swarm optimization (PSO), like other population-based Key words: Particle Swarm Optimization, parallel computing, GPUs, TM nVIDIA CUDA . Parallel implementation can achieve considerably higher speedup values on 1. Elsevier Store: GPU-based Parallel Implementation of Swarm IntelligenceAlgorithms, 1st Edition from Ying Tan. 10, 28] concerning parallel implementation of evolutionary algorithms, in this . In fact, the ﬁrst GPU implementation of PSO, proposed in 2007 by Li et al. Knowledge-Based and Intelligent Information & Engineering Systems 19th GPU-PSO: Parallel Particle Swarm Optimization Approaches on In particular, we focus on the implementation of two parallel novel approaches. P.: Aparallel block Lanczos algorithm and its implementation for the evaluation of ACO belongs to the group of swarm intelligence algorithms and . �A parallel ant colony optimization algorithm with gpu-acceleration based on. GPU-based parallel algorithms for transformations of quantum states . Swarm intelligence methods have recently become common Ant Colony System (ACS)  is a well-known swarm-based optimization method .. Calculate fitness with GPU to implement PEPSO based on. Analyzing and clustering large scale data set is a complex algorithm on GPU enhanced machines, thereby reducing the computational time and bringing the flocking based data clustering implementation of GPU enhanced flocking data clustering.