SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM

Zi Xu (), Yingying Li () and Xingfang Zhao ()
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Zi Xu: Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, People's Republic of China
Yingying Li: Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, People's Republic of China
Xingfang Zhao: Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, People's Republic of China

Asia-Pacific Journal of Operational Research (APJOR), 2014, vol. 31, issue 04, 1-20

Abstract: This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimization problem, several unconstrained optimization problems and one typical simulation-based optimization problem, i.e., (s, S)-inventory problem.

Keywords: Simulation optimization; stochastic approximation; Robbins–Monro algorithm; asymptotic rate of convergence (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S0217595914500262

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