133 research outputs found
COCO: Performance Assessment
We present an any-time performance assessment for benchmarking numerical
optimization algorithms in a black-box scenario, applied within the COCO
benchmarking platform. The performance assessment is based on runtimes measured
in number of objective function evaluations to reach one or several quality
indicator target values. We argue that runtime is the only available measure
with a generic, meaningful, and quantitative interpretation. We discuss the
choice of the target values, runlength-based targets, and the aggregation of
results by using simulated restarts, averages, and empirical distribution
functions
Study of the Fractal decomposition based metaheuristic on low-dimensional Black-Box optimization problems
This paper analyzes the performance of the Fractal Decomposition Algorithm
(FDA) metaheuristic applied to low-dimensional continuous optimization
problems. This algorithm was originally developed specifically to deal
efficiently with high-dimensional continuous optimization problems by building
a fractal-based search tree with a branching factor linearly proportional to
the number of dimensions. Here, we aim to answer the question of whether FDA
could be equally effective for low-dimensional problems. For this purpose, we
evaluate the performance of FDA on the Black Box Optimization Benchmark (BBOB)
for dimensions 2, 3, 5, 10, 20, and 40. The experimental results show that
overall the FDA in its current form does not perform well enough. Among
different function groups, FDA shows its best performance on Misc. moderate and
Weak structure functions
Toward self-learning model-based EAs
Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostl
A robust Gauss-Newton algorithm for the optimization of hydrological models: benchmarking against industry-standard algorithms
Optimization of model parameters is a ubiquitous task in hydrological and environmental modeling. Currently, the environmental modeling community tends to favor evolutionary techniques over classical Newtonâtype methods, in the light of the geometrically problematic features of objective functions, such as multiple optima and general nonsmoothness. The companion paper (Qin et al., 2018, https://doi.org/10.1029/2017WR022488) introduced the robust GaussâNewton (RGN) algorithm, an enhanced version of the standard GaussâNewton algorithm that employs several heuristics to enhance its explorative abilities and perform robustly even for problematic objective functions. This paper focuses on benchmarking the RGN algorithm against three optimization algorithms generally accepted as âbest practiceâ in the hydrological community, namely, the LevenbergâMarquardt algorithm, the shuffled complex evolution (SCE) search (with 2 and 10 complexes), and the dynamically dimensioned search (DDS). The empirical case studies include four conceptual hydrological models and three catchments. Empirical results indicate that, on average, RGN is 2â3 times more efficient than SCE (2 complexes) by achieving comparable robustness at a lower cost, 7â9 times more efficient than SCE (10 complexes) by trading off some speed to more than compensate for a somewhat lower robustness, 5â7 times more efficient than LevenbergâMarquardt by achieving higher robustness at a moderate additional cost, and 12â26 times more efficient than DDS in terms of robustnessâperâfixedâcost. A detailed analysis of performance in terms of reliability and cost is provided. Overall, the RGN algorithm is an attractive option for the calibration of hydrological models, and we recommend further investigation of its benefits for broader types of optimization problems.Youwei Qin, Dmitri Kavetski, George Kuczer
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Game analytics - maximizing the value of player data
During the years of the Information Age, technological advances in the computers,
satellites, data transfer, optics, and digital storage has led to the collection of an
immense mass of data on everything from business to astronomy, counting on the
power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century,
data were stored on disparate structures and very rapidly became overwhelming. The
initial chaos led to the creation of structured databases and database management
systems to assist with the management of large corpuses of data, and notably, the
effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information
gathering.peer-reviewe
Integrated High-Resolution Modeling for Operational Hydrologic Forecasting
Current advances in Earth-sensing technologies, physically-based modeling, and computational processing, offer the promise of a major revolution in hydrologic forecastingâwith profound implications for the management of water resources and protection from related disasters. However, access to the necessary capabilities for managing information from heterogeneous sources, and for its deployment in robust-enough modeling engines, remains the province of large governmental agencies. Moreover, even within this type of centralized operations, success is still challenged by the sheer computational complexity associated with overcoming uncertainty in the estimation of parameters and initial conditions in large-scale or high-resolution models.
In this dissertation we seek to facilitate the access to hydrometeorological data products from various U.S. agencies and to advanced watershed modeling tools through the implementation of a lightweight GIS-based software package. Accessible data products currently include gauge, radar, and satellite precipitation; stream discharge; distributed soil moisture and snow cover; and multi-resolution weather forecasts. Additionally, we introduce a suite of open-source methods aimed at the efficient parameterization and initialization of complex geophysical models in contexts of high uncertainty, scarce information, and limited computational resources. The developed products in this suite include: 1) model calibration based on state of the art ensemble evolutionary Pareto optimization, 2) automatic parameter estimation boosted through the incorporation of expert criteria, 3) data assimilation that hybridizes particle smoothing and variational strategies, 4) model state compression by means of optimized clustering, 5) high-dimensional stochastic approximation of watershed conditions through a novel lightweight Gaussian graphical model, and 6) simultaneous estimation of model parameters and states for hydrologic forecasting applications.
Each of these methods was tested using established distributed physically-based hydrologic modeling engines (VIC and the DHSVM) that were applied to watersheds in the U.S. of different sizesâfrom a small highly-instrumented catchment in Pennsylvania, to the basin of the Blue River in Oklahoma. A series of experiments was able to demonstrate statistically-significant improvements in the predictive accuracy of the proposed methods in contrast with traditional approaches. Taken together, these accessible and efficient tools can therefore be integrated within various model-based workflows for complex operational applications in water resources and beyond
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