Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Soft Computing - A Fusion of Foundations, Methodologies and Applications
GPU-based parallel particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimization with triggered mutation and its implementation based on GPU
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
GPU-based island model for evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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With the advent of the cards GPU, many computational problems have suffered from a net increase of performance. Nevertheless, the improvement depends strongly on the usage of the technology and the porting process used in the adaptation of the problem. These aspects are critical in order that the improvement of the performance of the code adapted to GPU is significant. This article focus on the study of the strategies for the porting of Particle Swarm Algorithm with parallel-evaluation of Schwefel Problem 1.2 and Rosenbrock function. The implementation evaluates the population in GPU, whereas the other intrinsic operators of the algorithm are executed in CPU. The design, the implementation and the associated issues related to GPU execution context are evaluated and presented. The results demonstrate the effectiveness of the proposed approach and its capability to effectively exploit the architecture of GPU.