A multi-objective evolutionary approach to simulation-based optimisation of real-world problems.

This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.University of Skövde Knowledge Foundation Swede

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics

Application of Surrogate Based Optimisation in the Design of Automotive Body Structures

The rapid development of automotive industry requires manufacturers to continuously reduce the development cost and time and to enhance the product quality. Thus, modern automotive design pays more attention to using CAE analysis based optimisation techniques to drive the entire design flow. This thesis focuses on the optimisation design to improve the automotive crashworthiness and fatigue performances, aiming to enhance the optimisation efficiency, accuracy, reliability, and robustness etc. The detailed contents are as follows: (1) To excavate the potential of crash energy absorbers, the concept of functionally graded structure was introduced and multiobjective designs were implemented to this novel type of structures. First, note that the severe deformation takes place in the tubal corners, multi-cell tubes with a lateral thickness gradient were proposed to better enhance the crashworthiness. The results of crashworthiness analyses and optimisation showed that these functionally graded multi-cell tubes are preferable to a uniform multi-cell tube. Then, functionally graded foam filled tubes with different gradient patterns were analyzed and optimized subject to lateral impact and the results demonstrated that these structures can still behave better than uniform foam filled structures under lateral loading, which will broaden the application scope of functionally graded structures. Finally, dual functionally graded structures, i.e. functionally graded foam filled tubes with functionally graded thickness walls, were proposed and different combinations of gradients were compared. The results indicated that placing more material to tubal corners and the maximum density to the outmost layer are beneficial to achieve the best performance. (2) To make full use of training data, multiple ensembles of surrogate models were proposed to maximize the fatigue life of a truck cab, while the panel thicknesses were taken as design variables and the structural mass the constraint. Meanwhile, particle swarm optimisation was integrated with sequential quadratic programming to avoid the premature convergence. The results illustrated that the hybrid particle swarm optimisation and ensembles of surrogates enable to attain a more competent solution for fatigue optimisation. (3) As the conventional surrogate based optimisation largely depends on the number of initial sample data, sequential surrogate modeling was proposed to practical applications in automotive industry. (a) To maximize the fatigue life of spot-welded joints, an expected improvement based sequential surrogate modeling method was utilized. The results showed that by using this method the performance can be significantly improved with only a relatively small number of finite element analyses. (c) A multiojective sequential surrogate modeling method was proposed to address a multiobjective optimisation of a foam-filled double cylindrical structure. By adding the sequential points and updating the Kriging model adaptively, more accurate Pareto solutions are generated. (4) While various uncertainties are inevitably present in real-life optimisations, conventional deterministic optimisations could probably lead to the violation of constraints and the instability of performances. Therefore, nondeterministic optimisation methods were introduced to solve the automotive design problems. (a) A multiobjective reliability-based optimisation for design of a door was investigated. Based on analysis and design responses surface models, the structural mass was minimized and the vertical sag stiffness was maximized subjected to the probabilistic constraint. The results revealed that the Pareto frontier is divided into the sensitive region and insensitive region with respect to uncertainties, and the decision maker is recommended to select a solution from the insensitive region. Furthermore, the reduction of uncertainties can help improve the reliability but will increase the manufacturing cost, and the tradeoff between the reliability target and performance should be made. (b) A multiobjective uncertain optimisation of the foam-filled double cylindrical structure was conducted by considering randomness in the foam density and wall thicknesses. Multiobjective particle swarm optimisation and Monte Carlo simulation were integrated into the optimisation. The results proved that while the performances of the objectives are sacrificed slightly, the nondeterministic optimisation can enhance the robustness of the objectives and maintain the reliability of the constraint. (c) A multiobjective robust optimisation of the truck cab was performed by considering the uncertainty in material properties. The general version of dual response surface model, namely dual surrogate model, was proposed to approximate the means and standard deviations of the performances. Then, the multiobjective particle optimisation was used to generate the well-distributed Pareto frontier. Finally, a hybrid multi-criteria decision making model was proposed to select the best compromise solution considering both the fatigue performance and its robustness. During this PhD study, the following ideas are considered innovative: (1) Surrogate modeling and multiobjective optimisation were integrated to address the design problems of novel functionally graded structures, aiming to develop more advanced automotive energy absorbers. (2) The ensembles of surrogates and hybrid particle swarm optimisation were proposed for the design of a truck cab, which could make full use of training points and has a strong searching capacity. (3) Sequential surrogate modeling methods were introduced to several optimisation problems in the automotive industry so that the optimisations are less dependent on the number of initial training points and both the efficiency and accuracy are improved. (4) The surrogate based optimisation method was implemented to address various uncertainties in real life applications. Furthermore, a hybrid multi-criteria decision making model was proposed to make the best compromise between the performance and robustness

Multi-Fidelity Methods for Optimization: A Survey

Real-world black-box optimization often involves time-consuming or costly experiments and simulations. Multi-fidelity optimization (MFO) stands out as a cost-effective strategy that balances high-fidelity accuracy with computational efficiency through a hierarchical fidelity approach. This survey presents a systematic exploration of MFO, underpinned by a novel text mining framework based on a pre-trained language model. We delve deep into the foundational principles and methodologies of MFO, focusing on three core components -- multi-fidelity surrogate models, fidelity management strategies, and optimization techniques. Additionally, this survey highlights the diverse applications of MFO across several key domains, including machine learning, engineering design optimization, and scientific discovery, showcasing the adaptability and effectiveness of MFO in tackling complex computational challenges. Furthermore, we also envision several emerging challenges and prospects in the MFO landscape, spanning scalability, the composition of lower fidelities, and the integration of human-in-the-loop approaches at the algorithmic level. We also address critical issues related to benchmarking and the advancement of open science within the MFO community. Overall, this survey aims to catalyze further research and foster collaborations in MFO, setting the stage for future innovations and breakthroughs in the field.Comment: 47 pages, 9 figure

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Conceptual multidisciplinary design via a multi-objective multi-fidelity optimisation method.

Air travel demand and the associated fuel emissions are expected to keep increasing in the following decades, forcing the aerospace industry to find ways to revolutionise the design process to achieve step-like performance improvements and emission reduction goals. A promising approach towards that goal is multidisciplinary design. To maximise the benefits, interdisciplinary synergies have to be investigated early in the design process. Efficient multidisciplinary optimisation tools are required to reliably identify a set of promising design directions to support engineering decision making towards the new generation of aircraft. To support these needs, a novel optimisation methodology is proposed aiming in exploiting multidisciplinary trends in the conceptual stage, exploring the design space and providing a pareto set of optimum configurations in the minimum cost possible. This is achieved by a combination of the expected improvement surrogate based optimisation plan, a novel Kriging modification to allow the use of multi-fidelity tools and a multi-objective sub-optimisation process infill formulation implemented within an multidisciplinary design optimisation architecture. A series of analytical test cases were initially used to develop the methodology and examine its performance under a set of criteria like global optimality, computational efficiency and dimensionality scaling. These were followed by two industrially relevant aerodynamic design cases, the RAE2822 transonic airfoil and the GARTEUR high lift configuration, investigating the effect of the constraint handling methods and the low fidelity tool. The cost reductions and exploration characteristics achieved by the method were quantified in realistic unconstrained, constrained and multi-objective problems. Finally, an aerostructural optimisation study of the NASA Common Research Model was used as a representative of a complex multidisciplinary design problem. The results demonstrate the framework’s capabilities in industrial problems, showing improved results and design space exploration but with lower costs than similarly oriented methods. The effect of the multidisciplinary architecture was also examined

Reliability-based design and topology optimization of aerospace components and structures

Programa de doutoramento en Enxeñaría Civil. 5011V01[Abstract] This work presents a research on Reliability Analysis (RA) and Reliability-Based Design Optimization (RBDO) with the target of solving practical aerospace structures problems. Two types of problems are considered, each one solved with a different approach. The first one applies Reliability-Based Topology Optimization (RBTO) to industry-like aerospace structures, while the second performs RBDO on aerospace components that require meticulous and computationally expensive simulations in order to predict accurately their behaviour. The RBDO method used in both approaches is the Sequential Optimization and Reliability Assessment (SORA) due to its uncoupled nature, which allows to separate the Deterministic Optimization (DO) and RA phases being easily combinable with external software. On the other hand, the RA method selected is the Hybrid Mean Value (HMV) given its robustness. Both algorithms have been implemented in MATLAB codes working in a High Performance Computing (HPC) environment. In the first approach they are combined with an external optimization software (Altar Optistruct) able to perform Deterministic Topology Optimization (DTO) problems. In the second approach the MATLAB codes are combined with external Finite Element (FE) analysis software (Abaqus), and the RA phase benefits from a Polynomial Chaos Expansion (PCE) based metamodel in order to save computating time. Several application examples have been carried out, including a wing, a rear fuselage and a pylon in the first approach and a stiffened composite panel in the second approach.[Resumen] Este trabajo presenta una investigación en análisis de fiabilidad (RA) y diseño óptimo en entornos de incertidumbre (RBDO) con el objetivo de resolver problemas prácticos de estructuras aeroespaciales. Se han considerado dos tipos de problema, cada uno siendo resuelto con un enfoque diferente. En el primero se aplica optimización topológica en entornos de incertidumbre (RBTO) a estructuras aeroespaciales similares a las que se pueden encontrar en un contexto industrial, mientras que el segundo aplica RBDO a componentes aeroespaciales que requieren simulaciones minuciosas y costosas computacionalmente para predecir correctamente su comportamiento. El método de RBDO usado en ambos enfoques es el de optimización y evaluación de fiabilidad secuencial (SORA) debido a su naturaleza desacoplada, que permite separar las fases de optimización determinista (DO) y RA siendo fácilmente combinable con software externos. Por otro lado, el método de RA seleccionado es el valor medio híbrido (HMV) debido a su robustez. Ambos algoritmos se han implementado en códigos de MATLAB que trabajan en ambientes de computación de alto rendimiento (HPC). En el primer enfoque se combinan con un software de optimización externo (Altair Optistruct) capaz de resolver problemas de optimización topológica determinista (DTO). En el segundo enfoque los códigos de MATLAB se combinan con un software de análisis de elementos finitos (FE) externo (Abaqus), y la fase de RA se beneficia de un metamodelo basado en la expansión polinómica del caos (PCE) para ahorrar coste computacional. En este marco de referencia, se han resuelto varios ejemplos, que incluyen un ala, la parte trasera de un fuselaje y un pilono para el primer enfoque y un panel rigidizado de material compuesto en el segundo enfoque.[Resumo] Este traballo presenta unha investigación en análise de fiabilidade (RA) e deseño óptimo en contornas de incerteza (RBDO) co obxectivo de resolver problemas prácticos de estruturas aeroespaciais. Consideráronse dous tipos de problema, cada un sendo resolto cun enfoque diferente. No primeiro aplícase optimización topolóxica en contornas de incerteza (RBTO) a estruturas aeroespaciais similares ás que se poden atopar nun contexto industrial, mentres que o segundo aplica RBDO a compoñentes aeroespaciais que requiren simulacións minuciosas e custosas computacionalmente para predicir correctamente o seu comportamento. O método de RBDO usado en ambos os enfoques é o de optimización e avaliación de fiabilidade secuencial (SORA) debido á súa natureza desacoplada, que permite separar as fases de optimización determinista (DO) e RA sendo facilmente combinable con software externos. Doutra banda, o método de RA seleccionado é o valor medio híbrido (HMV) debido á súa robustez. Ambos os algoritmos implementáronse en códigos de MATLAB que traballan en ambientes de computación de alto rendemento (HPC). No primeiro enfoque combínanse cun software de optimización externo (Altair Optistruct) capaz de resolver problemas de optimización topolóxica determinista (DTO). No segundo enfoque os códigos de MATLAB combínanse cun software de análise de elementos finitos (FE) externo (Abaqus), e a fase de RA benefíciase dun metamodelo baseado na expansión polinómica do caos (PCE) para aforrar custo computacional. Neste marco de referencia, resolvéronse varios exemplos, que inclúen un á, a parte traseira dunha fuselaxe e un pilono para o primeiro enfoque e un panel rixidizado de material composto no segundo enfoque