Optimizing Optimization for Design Optimization

Krus, Petter; Andersson, Johan

doi:10.1115/detc2003/dac-48803

Cited by 30 publications

(27 citation statements)

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“…In particular it is very suitable for simulation-based optimization. It has been applied to problems ranging from aircraft actuation systems, to industrial robots and hydraulic machinery, where simulation models have been used to calculate the objective function, see for example Krus and Andersson (2003). In this paper it has also been turned towards itself since it was also used to tune its own parameters.…”

Section: Discussionmentioning

confidence: 99%

“…This criterion was presented Krus and Andersson (2003) and also by Schutte et al (2005). However, for some objective functions it is so simple to find the optimum that the probability of finding the right optimum is P opt =1 (as in the first test function).…”

Section: Performance Index Of An Optimization Algorithmmentioning

confidence: 99%

“…Smit and Eiben (2009) use different methods to tune parameters in an evolutionary algorithm, including evolutionary algorithm itself. In Krus and Andersson (2003) used meta-optimization of a modified version of the Complex method using a criterion combining both number of objective function evaluations and the probability of finding the correct optimum…”

Section: Meta-optimizationmentioning

confidence: 99%

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

Krus

Ölvander

2013

Engineering Optimization

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Design optimization is becoming an increasingly important tool for design, often using simulation as part of the evaluation of the objective function. A measure of the efficiency of an optimization algorithm is of great importance when comparing methods. The main contribution of this paper is the introduction of a singular performance criteria, the Entropy Rate Index, based on Shannon's information theory, taking both reliability and rate of convergence into account. It can also be used to characterize the difficulty of different optimization problems. Such a performance criterion can also be used for optimization of the optimization algorithms itself.In this paper the Complex-RF optimization method is described and its performance evaluated and optimized using the established performance criterion.Finally, in order to be able to predict the resources needed for optimization an objective function temperament factor, is defined that indicates the degree of difficulty of the objective function.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Index Of An Optimization Algorithmmentioning

confidence: 99%

Section: Meta-optimizationmentioning

confidence: 99%

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

Krus

Ölvander

2013

Engineering Optimization

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“…4.2. The objective function of the design optimisation should reflect the de- [48] sired characteristics of the final gearbox. The minimisation of consumed energy is central to this methodology, but the objective function may also include other aspects, such as the ones discussed in section 2.1.…”

Section: Simulation-based Optimisationmentioning

confidence: 99%

Design Automation of Complex Hydromechanical Transmissions

Pettersson¹

2013

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This thesis proposes an automated methodology for the design of complex multiple-mode hydromechanical transmissions. High fuel prices and strict emission regulations are today drivers of the development of new fuel-efficient drive transmissions for construction machinery. Hydromechanical transmissions have high energy efficiency and a wide torque/speed conversion range. They are today strong candidates to replace the fuel-thirsty torque converters conventionally used in heavy construction machines. The trend towards more complex transmission architectures increases the need for more sophisticated product development methods. Complex multiple-mode transmissions are difficult to design and prototype and can be realised in a great number of different architectures. This increases the need for reliable concept evaluation in early design stages. The design of the transmission is also strongly coupled to its energy consumption and for a fair comparison between transmission concepts optimal designs are necessary.Design automation and optimisation with detailed simulation models can support the industrial engineer in the design task and increase the available knowledge early in the design process. The proposed methodology uses simulation-based optimisation to design the transmission for a specific vehicle application. Various aspects of the transmission's characteristics may be targeted, although energy efficiency is in great focus in this work. To evaluate the energy efficiency, the transmission designs are simulated using backward-facing simulations with detailed power loss models. The methodology is applicable for designing the drive transmissions of construction machines and other mobile working vehicles such as agricultural machines, forest machines and mobile mining equipment.

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“…[105]) address the reflection behaviour; in the Complex-RF variant [106], a random element and a forgetting factor favouring recently obtained solutions are included. Variants of the Complex algorithm have been successfully used for the optimization of for example hydrostatic transmission control [82], aircraft systems [106], industrial robots [107], and the design of hydro-mechanical transmissions [93].…”

Section: Optimization Algorithmmentioning

confidence: 99%

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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Optimizing Optimization for Design Optimization

Cited by 30 publications

References 0 publications

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

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

Design Automation of Complex Hydromechanical Transmissions

Simulation-Based Optimization of a Series Hydraulic Hybrid Vehicle

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