Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Ant Colony Optimization
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Gpu gems 3
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A survey on parallel ant colony optimization
Applied Soft Computing
3D object modeling with graphics hardware acceleration and unsupervised neural networks
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Parallel Ant Colony Optimization on Graphics Processing Units
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
A high performance parallel DCT with OpenCL on heterogeneous computing environment
Multimedia Tools and Applications
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
Parallel ant colony optimisation algorithm for continuous domains on graphics processing unit
International Journal of Computing Science and Mathematics
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This paper presents a massively parallel Ant Colony Optimization - Pattern Search (ACO-PS) algorithm with graphics hardware acceleration on nonlinear function optimization problems. The objective of this study is to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for ACO-PS. GPU, the common graphics hardware found in modern personal computers, can be used for data-parallel computing in a desktop setting. In this research, the classical ACO is adapted in the data-parallel GPU computing platform featuring 'Single Instruction - Multiple Thread' (SIMT). The global optimal search of the ACO is enhanced by the classical local Pattern Search (PS) method. The hybrid ACOPS method is implemented in a GPU+CPU hardware platform and compared to a similar implementation in a Central Processing Unit (CPU) platform. Computational results indicate that GPU-accelerated SIMT-ACO-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ACO-PS with GPU acceleration.