Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CEC.2014.6900461 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
hardware platforms,stochastic processes,random number generators,benchmark functions,random number generation,statistical analysis,pso,particle swarm optimisation,statistical properties,intelligent optimization algorithms,search problems,optimized libraries,particle swarm optimization algorithm,cpus,mersenne twister,gpus,stochastic search process,rngs,generators,optimization,benchmark testing,hardware,particle swarm optimization | Particle swarm optimization,Mathematical optimization,Algorithmic efficiency,CUDA,Computer science,Parallel computing,Algorithm,Uniform distribution (continuous),Multi-swarm optimization,Random number generation,Complex problems,Metaheuristic | Conference |
ISBN | Citations | PageRank |
978-1-4799-6626-4 | 4 | 0.42 |
References | Authors | |
7 | 2 | |