Abstract
The field of simulation optimization has seen algorithms proposed for local optimization, drawing upon different locally convergent search methods. Similarly, there are numerous global optimization algorithms with differing strategies to achieve convergence. In this paper, we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response. We propose Trust Region Based Optimization with Adaptive Restart (TBOAR), a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach. Additionally, we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories. This paper presents preliminary results showing the advantage of the proposed approach over the benchmark Efficient Global Optimization algorithm, focusing on a deterministic black box simulator with a d-dimensional input and a one-dimensional response.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 Winter Simulation Conference, WSC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2104-2115 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781538634288 |
| DOIs | |
| State | Published - Jan 4 2018 |
| Event | 2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States Duration: Dec 3 2017 → Dec 6 2017 |
Other
| Other | 2017 Winter Simulation Conference, WSC 2017 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 12/3/17 → 12/6/17 |
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ASJC Scopus subject areas
- Software
- Modeling and Simulation
- Computer Science Applications
Cite this
Trust region based stochastic optimization with adaptive restart : A family of global optimization algorithms. / Mathesen, Logan; Pedrielli, Giulia; Ng, Szu Hui.
2017 Winter Simulation Conference, WSC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2104-2115.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Trust region based stochastic optimization with adaptive restart
T2 - A family of global optimization algorithms
AU - Mathesen, Logan
AU - Pedrielli, Giulia
AU - Ng, Szu Hui
PY - 2018/1/4
Y1 - 2018/1/4
N2 - The field of simulation optimization has seen algorithms proposed for local optimization, drawing upon different locally convergent search methods. Similarly, there are numerous global optimization algorithms with differing strategies to achieve convergence. In this paper, we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response. We propose Trust Region Based Optimization with Adaptive Restart (TBOAR), a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach. Additionally, we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories. This paper presents preliminary results showing the advantage of the proposed approach over the benchmark Efficient Global Optimization algorithm, focusing on a deterministic black box simulator with a d-dimensional input and a one-dimensional response.
AB - The field of simulation optimization has seen algorithms proposed for local optimization, drawing upon different locally convergent search methods. Similarly, there are numerous global optimization algorithms with differing strategies to achieve convergence. In this paper, we look specifically into meta-model based algorithms that stochastically drive global search through an optimal sampling criteria evaluated over a constructed meta-model of the predicted response considering the uncertainty of the response. We propose Trust Region Based Optimization with Adaptive Restart (TBOAR), a family of algorithms that dynamically restarts a trust region based search method via an optimal sampling criteria derived upon a meta-model based global search approach. Additionally, we propose a new sampling criteria to reconcile undesirable adaptive restart trajectories. This paper presents preliminary results showing the advantage of the proposed approach over the benchmark Efficient Global Optimization algorithm, focusing on a deterministic black box simulator with a d-dimensional input and a one-dimensional response.
UR - http://www.scopus.com/inward/record.url?scp=85044543235&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044543235&partnerID=8YFLogxK
U2 - 10.1109/WSC.2017.8247943
DO - 10.1109/WSC.2017.8247943
M3 - Conference contribution
AN - SCOPUS:85044543235
SP - 2104
EP - 2115
BT - 2017 Winter Simulation Conference, WSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -