A comprehensive literature classification of simulation optimisation methods

Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measureSimulation Optimization, classification methods, literature survey

Contracts and Behavioral Patterns for SoS: The EU IP DANSE approach

This paper presents some of the results of the first year of DANSE, one of the first EU IP projects dedicated to SoS. Concretely, we offer a tool chain that allows to specify SoS and SoS requirements at high level, and analyse them using powerful toolsets coming from the formal verification area. At the high level, we use UPDM, the system model provided by the british army as well as a new type of contract based on behavioral patterns. At low level, we rely on a powerful simulation toolset combined with recent advances from the area of statistical model checking. The approach has been applied to a case study developed at EADS Innovation Works.Comment: In Proceedings AiSoS 2013, arXiv:1311.319

Solving optimisation problems in metal forming using Finite Element simulation and metamodelling techniques

During the last decades, Finite Element (FEM) simulations\ud of metal forming processes have become important\ud tools for designing feasible production processes. In more\ud recent years, several authors recognised the potential of\ud coupling FEM simulations to mathematical optimisation\ud algorithms to design optimal metal forming processes instead\ud of only feasible ones.\ud Within the current project, an optimisation strategy is being\ud developed, which is capable of optimising metal forming\ud processes in general using time consuming nonlinear\ud FEM simulations. The expression “optimisation strategy”\ud is used to emphasise that the focus is not solely on solving\ud optimisation problems by an optimisation algorithm, but\ud the way these optimisation problems in metal forming are\ud modelled is also investigated. This modelling comprises\ud the quantification of objective functions and constraints\ud and the selection of design variables.\ud This paper, however, is concerned with the choice for\ud and the implementation of an optimisation algorithm for\ud solving optimisation problems in metal forming. Several\ud groups of optimisation algorithms can be encountered in\ud metal forming literature: classical iterative, genetic and\ud approximate optimisation algorithms are already applied\ud in the field. We propose a metamodel based optimisation\ud algorithm belonging to the latter group, since approximate\ud algorithms are relatively efficient in case of time consuming\ud function evaluations such as the nonlinear FEM calculations\ud we are considering. Additionally, approximate optimisation\ud algorithms strive for a global optimum and do\ud not need sensitivities, which are quite difficult to obtain\ud for FEM simulations. A final advantage of approximate\ud optimisation algorithms is the process knowledge, which\ud can be gained by visualising metamodels.\ud In this paper, we propose a sequential approximate optimisation\ud algorithm, which incorporates both Response\ud Surface Methodology (RSM) and Design and Analysis\ud of Computer Experiments (DACE) metamodelling techniques.\ud RSM is based on fitting lower order polynomials\ud by least squares regression, whereas DACE uses Kriging\ud interpolation functions as metamodels. Most authors in\ud the field of metal forming use RSM, although this metamodelling\ud technique was originally developed for physical\ud experiments that are known to have a stochastic na-\ud ¤Faculty of Engineering Technology (Applied Mechanics group),\ud University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands,\ud email: [email protected]\ud ture due to measurement noise present. This measurement\ud noise is absent in case of deterministic computer experiments\ud such as FEM simulations. Hence, an interpolation\ud model fitted by DACE is thought to be more applicable in\ud combination with metal forming simulations. Nevertheless,\ud the proposed algorithm utilises both RSM and DACE\ud metamodelling techniques.\ud As a Design Of Experiments (DOE) strategy, a combination\ud of a maximin spacefilling Latin Hypercubes Design\ud and a full factorial design was implemented, which takes\ud into account explicit constraints. Additionally, the algorithm\ud incorporates cross validation as a metamodel validation\ud technique and uses a Sequential Quadratic Programming\ud algorithm for metamodel optimisation. To overcome\ud the problem of ending up in a local optimum, the\ud SQP algorithm is initialised from every DOE point, which\ud is very time efficient since evaluating the metamodels can\ud be done within a fraction of a second. The proposed algorithm\ud allows for sequential improvement of the metamodels\ud to obtain a more accurate optimum.\ud As an example case, the optimisation algorithm was applied\ud to obtain the optimised internal pressure and axial\ud feeding load paths to minimise wall thickness variations\ud in a simple hydroformed product. The results are satisfactory,\ud which shows the good applicability of metamodelling\ud techniques to optimise metal forming processes using\ud time consuming FEM simulations

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

The role of statistical methodology in simulation

statistical methods;simulation;operations research

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing