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Comparison of Random Number Generators in Particle Swarm Optimization Algorithm

Ke Ding, Ying Tan
Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, and Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing,100871, P.R. China
IEEE World Conference on Computational Intelligence (IEEE WCCI’2014) – IEEE Congress on Evolutionary Computation (CEC’2014), 2014

@article{ding2014comparison,

   title={Comparison of Random Number Generators in Particle Swarm Optimization Algorithm},

   author={Ding, Ke and Tan, Ying},

   year={2014}

}

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Intelligent optimization algorithms are very effective to tackle complex problems that would be difficult or impossible to solve exactly. A key component within these algorithms is the random number generators (RNGs) which provide random numbers to drive the stochastic search process. Much effort is devoted to develop efficient RNGs with good statistical properties, and many highly optimized libraries are ready to use for generating random numbers fast on both CPUs and other hardware platforms such as GPUs. However, few study is focused on how different RNGs can effect the performance of specific intelligent optimization algorithms. In this paper, we empirically compared 13 widely used RNGs with uniform distribution based on both CPUs and GPUs, with respect to algorithm efficiency as well as their impact on Particle Swarm Optimization (PSO). Two strategies were adopted to conduct comparison among multiple RNGs for multiple objectives. The experiments were conducted on well-known benchmark functions of diverse landscapes, and were run on the GPU for the purpose of accelerating. The results show that RNGs have very different efficiencies in terms of speed, and GPU-based RNGs can be much faster than their CPU-based counterparts if properly utilized. However, no statistically significant disparity in solution quality was observed. Thus it is reasonable to use more efficient RNGs such as Mersenne Twister. The framework proposed in this work can be easily extended to compare the impact of non-uniformly distributed RNGs on more other intelligent optimization algorithms.
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