ASTROMORF: adaptive sampling trust-region optimization with dimensionality reduction
ASTROMORF: adaptive sampling trust-region optimization with dimensionality reduction
High dimensional simulation optimization problems have become prevalent in recent years. In practice, the objective function is typically influenced by a lower dimensional combination of the original decision variables, and implementing dimensionality reduction can improve the efficiency of the optimization algorithm. In this paper, we introduce a novel algorithm ASTROMoRF that combines adaptive sampling with dimensionality reduction, using an iterative trust-region approach. Within a trust-region algorithm a series of surrogates or metamodels is built to estimate the objective function. Using a lower dimensional subspace reduces the number of design points needed for building a surrogate within each trust-region and consequently the number of simulation replications. We explain the basis for the algorithm within the paper and compare its finite-time performance with other state-of-the-art solvers.
simulation optimization, Dimensionality reduction
Rees, Benjamin Wilson
133a60a9-3fc7-4ee8-831b-1db502287af1
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Vuong, Phan Tu
52577e5d-ebe9-4a43-b5e7-68aa06cfdcaf
Rees, Benjamin Wilson
133a60a9-3fc7-4ee8-831b-1db502287af1
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Vuong, Phan Tu
52577e5d-ebe9-4a43-b5e7-68aa06cfdcaf
Rees, Benjamin Wilson, Currie, Christine and Vuong, Phan Tu
(2025)
ASTROMORF: adaptive sampling trust-region optimization with dimensionality reduction.
2025 Winter Simulation Conference, , Seattle, United States.
07 - 10 Dec 2025.
12 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
High dimensional simulation optimization problems have become prevalent in recent years. In practice, the objective function is typically influenced by a lower dimensional combination of the original decision variables, and implementing dimensionality reduction can improve the efficiency of the optimization algorithm. In this paper, we introduce a novel algorithm ASTROMoRF that combines adaptive sampling with dimensionality reduction, using an iterative trust-region approach. Within a trust-region algorithm a series of surrogates or metamodels is built to estimate the objective function. Using a lower dimensional subspace reduces the number of design points needed for building a surrogate within each trust-region and consequently the number of simulation replications. We explain the basis for the algorithm within the paper and compare its finite-time performance with other state-of-the-art solvers.
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Accepted/In Press date: 29 July 2025
Venue - Dates:
2025 Winter Simulation Conference, , Seattle, United States, 2025-12-07 - 2025-12-10
Keywords:
simulation optimization, Dimensionality reduction
Identifiers
Local EPrints ID: 506198
URI: http://eprints.soton.ac.uk/id/eprint/506198
PURE UUID: 323b3b74-8997-4696-b209-d4f99ddb6972
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Date deposited: 30 Oct 2025 17:33
Last modified: 31 Oct 2025 03:04
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Author:
Benjamin Wilson Rees
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