313 research outputs found
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified
Metamodeling sampling criteria in a global optimization framework
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76903/1/AIAA-2000-4921-115.pd
A new Taxonomy of Continuous Global Optimization Algorithms
Surrogate-based optimization, nature-inspired metaheuristics, and hybrid
combinations have become state of the art in algorithm design for solving
real-world optimization problems. Still, it is difficult for practitioners to
get an overview that explains their advantages in comparison to a large number
of available methods in the scope of optimization. Available taxonomies lack
the embedding of current approaches in the larger context of this broad field.
This article presents a taxonomy of the field, which explores and matches
algorithm strategies by extracting similarities and differences in their search
strategies. A particular focus lies on algorithms using surrogates,
nature-inspired designs, and those created by design optimization. The
extracted features of components or operators allow us to create a set of
classification indicators to distinguish between a small number of classes. The
features allow a deeper understanding of components of the search strategies
and further indicate the close connections between the different algorithm
designs. We present intuitive analogies to explain the basic principles of the
search algorithms, particularly useful for novices in this research field.
Furthermore, this taxonomy allows recommendations for the applicability of the
corresponding algorithms.Comment: 35 pages total, 28 written pages, 4 figures, 2019 Reworked Versio
A portfolio approach to massively parallel Bayesian optimization
One way to reduce the time of conducting optimization studies is to evaluate
designs in parallel rather than just one-at-a-time. For expensive-to-evaluate
black-boxes, batch versions of Bayesian optimization have been proposed. They
work by building a surrogate model of the black-box that can be used to select
the designs to evaluate efficiently via an infill criterion. Still, with higher
levels of parallelization becoming available, the strategies that work for a
few tens of parallel evaluations become limiting, in particular due to the
complexity of selecting more evaluations. It is even more crucial when the
black-box is noisy, necessitating more evaluations as well as repeating
experiments. Here we propose a scalable strategy that can keep up with massive
batching natively, focused on the exploration/exploitation trade-off and a
portfolio allocation. We compare the approach with related methods on
deterministic and noisy functions, for mono and multiobjective optimization
tasks. These experiments show similar or better performance than existing
methods, while being orders of magnitude faster
Improving the optimisation performance of an ensemble of radial basis functions
In this paper we investigate surrogate-based optimisation performance using two different ensemble approaches, and a novel update strategy based on the local Pearson correlation coefficient. The ?first ensemble, is based on a selective approach, where ns RBFs are constructed and the most accurate RBF is selected for prediction at each iteration, while the others are ignored. The secondensemble uses a combined approach, which takes advantage of ns different RBFs, in the hope of reducing errors in the prediction through a weighted combination of the RBFs used. The update strategy uses the local Pearson correlation coefficient as a constraint to ignore domain areas wherethere is disagreement between the surrogates. In total the performance of six different approaches are investigated, using ?five analytical test functions with 2 to 50 dimensions, and one engineering problem related to the frequency response of a satellite boom with 2 to 40 dimensions
Efficient design optimization of high-performance MEMS based on a surrogate-assisted self-adaptive differential evolution
High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane microactuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability
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