Performance index and meta-optimization of a direct search optimization method

Krus, Petter; Ölvander, Johan

doi:10.1080/0305215x.2012.725052

Cited by 19 publications

(29 citation statements)

References 24 publications

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“…The optimization problem contains nine design parameters (see Table 5.5). In comparison to the principal para- Table 5.6) matching closely those obtained through meta-optimization by Krus and Ölvander [115]. The results displayed in Figure 5.2 show fairly little variation in the principle design parameters, especially hydraulic pump/motor and combustion engine.…”

Section: Baseline Optimization Experimentssupporting

confidence: 50%

“…The information gathered is the information entropy H x . The derivation of its formula [115] is documented in paper [V]. The performance index Φ then relates the information entropy H x to an optimization effort measure N m .…”

Section: Optimization Performance Evaluationmentioning

confidence: 99%

“…The basic performance index definition can furthermore be modified (Φ (2) ) to include the probability of an optimization algorithm finding the optimal solution, the success rate P opt [115].…”

Section: Simulation-based Optimization Of a Series Hydraulic Hybrid Vmentioning

confidence: 99%

Section: Optimization Performance Evaluationmentioning

confidence: 99%

“…[114], for which a number of approaches are listed for example in [115,116]). Here, the Entropy-Rate-Based Performance Index (ERI) [115] is used, which is based on Shannon's [117] information theory. The design process is interpreted as an information-gathering process within the design space with the aim of reducing the inherent uncertainty in n design parameter values x i within the parameter range x i,R = x i,max − x i,min to defined limits:…”

Section: Optimization Performance Evaluationmentioning

confidence: 99%

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Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

Bær¹

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"The brick walls are there for a reason. (...) [They] are there to give us a chance to show how badly we want something. Randy Pausch AbstractHybrid transmissions are characterized by their utilization of more than one form of energy storage. They have the potential to help reduce overall fuel consumption and vehicle emissions by providing the possibility of brake energy recuperation and prime mover operation management. Electric hybrids and electric vehicle drives are nowadays ubiquitous, and mechanical energy storage in flywheel has been investigated in the past. The use of fluid power technology with a combustion engine has also been investigated since the late 1970s, and is frequently revisited.Hydraulic hybridization is especially attractive for heavy vehicles with frequent braking and acceleration which benefit most from fluid power components' high power density, typically busses, delivery or refuse vehicles, and vehicles with existing hydraulic circuits and transmissions, such as forest and construction machinery, but have been considered for smaller vehicles as well.Due to the characteristic discharge profile of hydraulic energy storage, special attention needs to be paid to control aspects in the design process to guarantee drivability of the vehicle. In this respect, simulation models can be of use in early design process stages for cheaper and faster evaluation of concepts and designs than physical experiments and prototyping, and to generate better understanding of the system studied. Engineering optimization aids in the systematic exploration of a given design space, to determine limits and potentials, evaluate trade-offs and potentially find unexpected solutions. In the optimization of a hydraulic hybrid transmission, the integration of component and controller design is of importance, and different strategies (sequential, iterative, bi-level and simultaneous approaches) are conceivable, with varying consequences for the implementation.This thesis establishes a simulation-based optimization framework for a hydraulic hybrid transmission with series architecture. Component and control parameter optimization are addressed simultaneously, using a rule-based supervisory control strategy. The forward-facing dynamic simulation model at the centre of the framework is built in Hopsan, a multi-disciplinary open-source tool developed at Linköping University. The optimization is set up and conducted for an example application of an on-road light-duty truck over standard drive i cycles. Both results from these experiments as well as the framework itself are studied and evaluated. Relevant design aspects, such as explicit design relations to be considered and performance requirements for more robust design, are identified and addressed, and the optimization problem is analysed with regard to algorithm performance and problem formulation. The final result is an optimization framework that can be adjusted for further in-depth studies, for example through the inclusion of additional components or optimization obj...

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Section: Baseline Optimization Experimentssupporting

confidence: 50%

Section: Optimization Performance Evaluationmentioning

confidence: 99%

Section: Simulation-based Optimization Of a Series Hydraulic Hybrid Vmentioning

confidence: 99%

Section: Optimization Performance Evaluationmentioning

confidence: 99%

Section: Optimization Performance Evaluationmentioning

confidence: 99%

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Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

Bær¹

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Design Space Configuration for Minimizing Design Information Entropy

Krus

2014

ICoRD’15 – Research Into Design Across Boundaries Volume 1

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Abstract:In this paper concepts for quantitative theory of design are developed. The design process is viewed as a process of increasing the information of the product/system using concept from information theory. The creation and evolution of design space and information entropy are central concepts that are studied in this paper. Furthermore, axiomatic design is discussed in relation to this, and it is shown that the two fundamental axioms are likely to be dependent.The design information entropy is used as a state that reflects both complexity and refinement, and it is argued that it can be useful as some measure of design effort and design quality. It provides a tool to describe the relation between a design, and the design space from which it is derived as well as for quantitative description for various aspects of design, both structural information regarding architecture and connectivity, as well as for parameter values, both discrete and continuous. In this paper it is also shown to be useful as a measure of quality when configuring design spaces, such as in product platforms, and when setting up parameterization for design.

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Optimization of the Complex-RFM optimization algorithm

Persson

Ölvander

2014

Optim Eng

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This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models with few variables. The modifications reduce the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is demonstrated for both analytical and engineering problems and compared with the performance of a number of existing algorithms. A meta-optimization of the algorithm is also performed to optimize its performance for arbitrary problems. To emphasize the fact that the modified algorithm uses metamodels it is denoted Complex-RFM.

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Performance index and meta-optimization of a direct search optimization method

Cited by 19 publications

References 24 publications

Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

Design Space Configuration for Minimizing Design Information Entropy

Optimization of the Complex-RFM optimization algorithm

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