Multiscale Chaotic SPSA and Smoothed Functional Algorithms for Simulation Optimization
Multiscale Chaotic SPSA and Smoothed Functional Algorithms for Simulation Optimization
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用于仿真优化的多尺度混沌 SPSA 和平滑函数算法
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
V. Borkar
中科院分区：
文献类型：
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作者：
S. Bhatnagar;V. Borkar
作者提出了一种双时间尺度版本的单模拟平滑泛函（SF）算法，并进行了额外的平均。他们还建议使用混沌的简单确定性迭代序列来生成用于平均的随机样本。该序列用于生成SF算法中N个独立且同分布（i.i.d）的高斯随机变量。并简要介绍了算法的收敛性分析。作者对混沌序列进行了数值实验，并与一个好的伪随机发生器进行了性能比较。接下来，他们展示了两种不同设置下的实验——一个带有反馈的M/G/1队列网络，以及在异步传输模式（ATM）网络中使用所有算法在可用比特率（ABR）服务中找到闭环最优策略（在预先指定的类中）的问题。作者观察到，在大多数情况下，使用混沌序列的算法比使用伪随机生成器的算法表现出更好的性能。
The authors propose a two-timescale version of the one-simulation smoothed functional (SF) algorithm with extra averaging. They also propose the use of a chaotic simple deterministic iterative sequence for generating random samples for averaging. This sequence is used for generating the N independent and identically distributed (i.i.d.), Gaussian random variables in the SF algorithm. The convergence analysis of the algorithms is also briefly presented. The authors show numerical experiments on the chaotic sequence and compare performance with a good pseudo-random generator. Next they show experiments in two different settings—a network of M/G/1 queues with feedback and the problem of finding a closed-loop optimal policy (within a prespecified class) in the available bit rate (ABR) service in asynchronous transfer mode (ATM) networks, using all the algorithms. The authors observe that algorithms that use the chaotic sequence show better performance in most cases than those that use the pseudo-random generator.