Meta-Optimization of Dimension Adaptive Parameter Schema for Nelder–Mead Algorithm in High-Dimensional Problems

Žiga Rojec, Tadej Tuma, Jernej Olenšek, Árpád Bűrmen and Janez Puhan
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Žiga Rojec: Department of Electronics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Tadej Tuma: Department of Electronics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Jernej Olenšek: Department of Electronics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Árpád Bűrmen: Department of Electronics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Janez Puhan: Department of Electronics, Faculty of Electrical Engineering, University of Ljubljana, SI-1000 Ljubljana, Slovenia

Mathematics, 2022, vol. 10, issue 13, 1-16

Abstract: Although proposed more than half a century ago, the Nelder–Mead simplex search algorithm is still widely used. Four numeric constants define the operations and behavior of the algorithm. The algorithm with the original constant values performs fine on most low-dimensional, but poorly on high-dimensional, problems. Therefore, to improve its behavior in high dimensions, several adaptive schemas setting the constants according to the problem dimension were proposed in the past. In this work, we present a novel adaptive schema obtained by a meta-optimization procedure. We describe a schema candidate with eight parameters subject to meta-optimization and define an objective function evaluating the candidate’s performance. The schema is optimized on up to 100-dimensional problems using the Parallel Simulated Annealing with Differential Evolution global method. The obtained global minimum represents the proposed schema. We compare the performance of the optimized schema with the existing adaptive schemas. The data profiles on the Gao–Han modified quadratic, Moré–Garbow–Hilstrom, and CUTEr (Constrained and Unconstrained Testing Environment, revisited) benchmark problem sets show that the obtained schema outperforms the existing adaptive schemas in terms of accuracy and convergence speed.

Keywords: meta-optimization; Nelder–Mead Algorithm; adaptive parameter schema; high-dimensional optimization problems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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