Refine
Document Type
- Working Paper (13) (remove)
Has Fulltext
- yes (13)
Keywords
- Optimization (4)
- Modeling (3)
- Benchmarking (2)
- Optimierung (2)
- Abgasreinigung (1)
- Artificial intelligence (1)
- Automation (1)
- Big Data (1)
- Big data platform (1)
- Business Intelligence (1)
- Cognition (1)
- Conditional inference tree (1)
- Data-Warehouse-Konzept (1)
- Decision tree (1)
- Design of Experiments (1)
- Discrete Optimization (1)
- Electrostatic Precipitator (1)
- Evolutionary Computation (1)
- Expensive Optimization (1)
- Faserverbundwerkstoffe (1)
- Flowcurve (1)
- Funktionstest (1)
- Health condition monitoring (1)
- Hot rolling (1)
- Imputation (1)
- Industrie 4.0 (1)
- Industry 4.0 (1)
- Knowledge extraction (1)
- Kognition (1)
- Kriging (1)
- Künstliche Intelligenz (1)
- Machine learning (1)
- Meta-model (1)
- Metal (1)
- Metamodels (1)
- Modelierung (1)
- Muschelknautz Method of Modelling (1)
- Neural and Evolutionary Computing (1)
- Parallelization (1)
- Performance (1)
- Referenzmodell (1)
- SAP (1)
- Signalanalyse (1)
- Simulation (1)
- Standardisierung (1)
- Staubabscheider (1)
- Structural Health Monitoring (1)
- Surrogate (1)
- Surrogate model based optimization (1)
- Surrogates (1)
- Tauchrohrtiefe (1)
- Test Function (1)
- Test function generator (1)
- Time Series (1)
- Univariate Data (1)
Institute
- Fakultät für Informatik und Ingenieurwissenschaften (F10) (13) (remove)
The availability of several CPU cores on current computers enables
parallelization and increases the computational power significantly.
Optimization algorithms have to be adapted to exploit these highly
parallelized systems and evaluate multiple candidate solutions in
each iteration. This issue is especially challenging for expensive
optimization problems, where surrogate models are employed to
reduce the load of objective function evaluations.
This paper compares different approaches for surrogate modelbased
optimization in parallel environments. Additionally, an easy
to use method, which was developed for an industrial project, is
proposed. All described algorithms are tested with a variety of
standard benchmark functions. Furthermore, they are applied to
a real-world engineering problem, the electrostatic precipitator
problem. Expensive computational fluid dynamics simulations are
required to estimate the performance of the precipitator. The task
is to optimize a gas-distribution system so that a desired velocity
distribution is achieved for the gas flow throughout the precipitator.
The vast amount of possible configurations leads to a complex
discrete valued optimization problem. The experiments indicate
that a hybrid approach works best, which proposes candidate solutions
based on different surrogate model-based infill criteria and
evolutionary operators.
This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes declarative goals of the user, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and varying use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case.
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well- specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.