Search results for "Metamodel"
showing 10 items of 83 documents
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
2023
Abstract For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to pr…
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies
2018
We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary a…
On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization
2019
Many works on surrogate-assisted evolutionary multiobjective optimization have been devoted to problems where function evaluations are time-consuming (e.g., based on simulations). In many real-life optimization problems, mathematical or simulation models are not always available and, instead, we only have data from experiments, measurements or sensors. In such cases, optimization is to be performed on surrogate models built on the data available. The main challenge there is to fit an accurate surrogate model and to obtain meaningful solutions. We apply Kriging as a surrogate model and utilize corresponding uncertainty information in different ways during the optimization process. We discuss…
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm
2019
We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. We are motivated by practical applicability and focus on two main challenges faced by practitioners in industry: 1) meaningful formulation of the optimization problem reflecting the needs of a decision maker and 2) finding a desirable solution based on a decision maker’s preferences when solving a problem with computationally expensive function evaluations. For the first challenge, we describe the procedure of modelling a component in the air intake ventilation system wi…
Handling expensive multiobjective optimization problems with evolutionary algorithms
2017
Multiobjective optimization problems (MOPs) with a large number of conflicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations or costly experiments), which pose an extra challenge in solving them. In this thesis, we first present a survey of different methods proposed in the literature to handle MOPs with expensive evaluations. We observed that most of the existing methods cannot be easily applied to problems with more than three objectives. Therefore, we propose a Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for problems with at least three expensive objectives. The alg…
Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization
2022
In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-base…
Editor Definition Language and Its Implementation
2001
Universal graphical editor definition language based on logical metamodel extended by presentation classes is proposed. Implementation principles of this language, based on Graphical Diagramming Engine are described.
The IEEE-FIPA Standard on the Design Process Documentation Template
2014
Nowadays, it is a matter of fact that a “one-size-fit-all” methodology or design process useful and fitting every kind of problem, situation, or design context does not exist. (Situational) Method Engineering (SME) discipline aims at determining techniques and tools for developing ad hoc design methodologies. SME mainly and highly focuses on the reuse of portion of existing design processes or methodologies (the method fragments). In order to have means for creating SME techniques and tools and for creating new design processes, some key elements are needed: a unique process metamodel for representing design processes and fragments, a proper template for the description of AO design process…
The Art of Bootstrapping
2020
Language workbenches are used to define languages using appropriate meta-languages. Meta-languages are also just languages and can, therefore, be defined using themselves. The process is called bootstrapping and is often difficult to achieve.
An organisational approach to engineer emergence within holarchies
2010
An open issue in self-organisation is how to engineer emergent behaviours. This issue is also of interest for engineering holonic multi-agent systems as any level of a holarchy is dependant of the emergent behaviours of its sub-levels. In order to tackle this specific feature of holonic multi-agent systems, the capacity concept which abstracts a know-how from its concrete realisation is introduced. The use of this concept is illustrated in this paper through a case study using the ASPECS development process which enables the analysis, design, implementation and deployment of holonic multi-agent systems and integrates the capacity as a core concept of it is underlying metamodel, called capac…