Benchmarking Global Optimizers

Antoine Arnoud (), Fatih Guvenen and Tatjana Kleineberg

No 801, Working Papers from Federal Reserve Bank of Minneapolis

Abstract: We benchmark six global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as on a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL), (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The sixth algorithm is TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Nonlinear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use benchmarking tools recently developed in the optimization literature. We find that the success rate of many optimizers varies dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer both on the test functions and the economic application. The next-best performing optimizers are StoGo for the test functions and MLSL and ISRES for the economic application.

Keywords: Parallelized optimizer; NLopt; Calibration; Estimation; Multistart algorithms; Global optimization (search for similar items in EconPapers)
JEL-codes: C61 C63 D58 (search for similar items in EconPapers)
Date: 2023-12-15
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmwp:97507

DOI: 10.21034/wp.801

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