Daniel Leal Souza
ABSTRACT
This work presents a PSO implemention in CUDA architecture, aiming to speed up the algorithm on problems which has large amounts of data. PSO-GPU algorithm was designed to customization, in order to adapt for any problem that can be solved by a PSO algorithm. By implementing PSO using CUDA architecture, each processing core of the GPU will be responsible for a portion of the overall processing operation, where each one of these pieces are handled and executed in a massive parallel enviroment, opening the possibility for solving problems that require a large processing load in considerably less time. In order to evaluate the performance of PSO-GPU algorithm two functions were used, both global optimization problems, where without constraints (Griewank function) and other considering constraints, the Welded Beam Design (WBD).
- Kennedy, J. and Eberhart, R. Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, (Perth, Australia), (1995), 1942--1948. IEEE Press.Google Scholar
Cross Ref
- Heppner, F. and Grenader, U. A Stochastic Nonlinear Model For Coordinated Bird Flocks: The Ubiquity of Chaos. AAAS Publications, Washington, DC, 1990.Google Scholar
- Kennedy, J. and Eberhart, R. A Discrete Binary Version of The Particle Swarm Algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, (Piscataway, New Jersey, USA), (1997), 4104--4108. IEEE Press.Google ScholarCross Ref
- He, Q., Wang, L. An Effective Co-evolutionary Particle Swarm Optimization for Constrained Engineering Design Problem. Engineering Aplications of Artificial Intellifence, (2007), 89--99, Elsevier Press. Google ScholarDigital Library
- Coello, C. A. C., Montes, E. M. Constraint-handling in Genetic Algorithms through the use of Dominance-based Tournament Selection. Advanced Engineering Informatics 16, (2002), 193--203.Google ScholarCross Ref
- Coello, C. A. C. Use of a Self-adaptive Penalty Approach for Engineering Optimization Problemas. Computers in Industry 41, (2000), 113--127. Google ScholarDigital Library
- Deb, K. GeneAS: a robust optimal design technique for mechanical component design. Dasgupta, D., Michalewicz, Z (Eds.), Evolutionary Algorithms in Engineering Applications. (Springer, Berlin), (1997), 497--514.Google Scholar
Index Terms
- PSO-GPU: accelerating particle swarm optimization in CUDA-based graphics processing units
Recommendations
Collaborative multi-swarm PSO for task matching using graphics processing units
GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computationWe investigate the performance of a highly parallel Particle Swarm Optimization (PSO) algorithm implemented on the GPU. In order to achieve this high degree of parallelism we implement a collaborative multi-swarm PSO algorithm on the GPU which relies on ...
SIMD Monte-Carlo Numerical Simulations Accelerated on GPU and Xeon Phi
The efficiency of a pleasingly parallel application is studied for several computing platforms. A real world problem, i.e., Monte-Carlo numerical simulations of stratospheric balloon envelope drift descent is considered. We detail the optimization of ...
Evaluation of parallel particle swarm optimization algorithms within the CUDATM architecture
Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically parallel and can be effectively implemented on Graphics Processing Units (GPUs), which are, in fact, massively parallel processing architectures. In this ...
Comments