Geometric guides for interactive evolutionary design

This thesis describes the addition of novel Geometric Guides to a generative Computer-Aided Design (CAD) application that supports early-stage concept generation. The application generates and evolves abstract 3D shapes, used to inspire the form of new product concepts. It was previously a conventional Interactive Evolutionary system where users selected shapes from evolving populations. However, design industry users wanted more control over the shapes, for example by allowing the system to influence the proportions of evolving forms. The solution researched, developed, integrated and tested is a more cooperative human-machine system combining classic user interaction with innovative geometric analysis. In the literature review, different types of Interactive Evolutionary Computation (IEC), Pose Normalisation (PN), Shape Comparison, and Minimum-Volume Bounding Box approaches are compared, with some of these technologies identified as applicable for this research. Using its Application Programming Interface, add-ins for the Siemens NX CAD system have been developed and integrated with an existing Interactive Evolutionary CAD system. These add-ins allow users to create a Geometric Guide (GG) at the start of a shape exploration session. Before evolving shapes can be compared with the GG, they must be aligned and scaled (known as Pose Normalisation in the literature). Computationally-efficient PN has been achieved using geometric functions such as Bounding Box for translation and scaling, and Principle Axes for the orientation. A shape comparison algorithm has been developed that is based on the principle of non-intersecting volumes. This algorithm is also implemented with standard, readily available geometric functions, is conceptually simple, accessible to other researchers and also offers appropriate efficacy. Objective geometric testing showed that the PN and Shape Comparison methods developed are suitable for this guiding application and can be efficiently adapted to enhance an Interactive Evolutionary Design system. System performance with different population sizes was examined to indicate how best to use the new guiding capabilities to assist users in evolutionary shape searching. This was backed up by participant testing research into two user interaction strategies. A Large Background Population (LBP) approach where the GG is used to select a sub-set of shapes to show to the user was shown to be the most effective. The inclusion of Geometric Guides has taken the research from the existing aesthetic focused tool to a system capable of application to a wider range of engineering design problems. This system supports earlier design processes and ideation in conceptual design and allows a designer to experiment with ideas freely to interactively explore populations of evolving solutions. The design approach has been further improved, and expanded beyond the previous quite limited scope of form exploration

Mining, Understanding and Integrating User Preferences in Software Refactoring Using Computational Search, Machine Learning, and Dimensionality Reduction

Search-Based Software Engineering (SBSE) is a software development practice which focuses on couching software engineering problems as optimization problems using metaheuristic techniques to automate the search for near optimal solutions to those problems. While SBSE has been successfully applied to a wide variety of software engineering problems, our understanding of the extent and nature of how software engineering problems can be formulated as automated or semi-automated search is still lacking. The majority of software engineering solutions are very subjective and present difficulties to formally define fitness functions to evaluate them. Current studies focus on guiding the search of optimal solutions rather than performing it. It is unclear yet the degree of interaction required with software engineers during the optimization process and how to reduce it. In this work, we focus on search-based software maintenance and evolution problems including software refactoring and software remodularization to improve the quality of systems. We propose to address the following challenges: • A major challenge in adapting a search-based technique for a software engineering problem is the definition of the fitness function. In most cases, fitness functions are ill-defined or subjective. • Most existing refactoring studies do not include the developer in the loop to analyze suggested refactoring solutions, and give their feedback during the optimization process. In addition, some quality metrics are cost-expensive leading to cost-expensive fitness functions. Moreover, while quality metrics evaluate the structural improvements of the refactored system, it is impossible to evaluate the semantic coherence of the design without user interactions. • Finally, several metrics can be dependent and correlated, thus it may be possible to reduce the number of objectives/dimensions when addressing refactoring problems. To address the above challenges, this work provides new techniques and tools to formulate software refactoring as scalable and learning-based search problem. We proposed novel interactive learning-based techniques using machine learning to incorporate developers knowledge and preferences in the search, resulting in more efficient and cost-effective search-based refactoring recommendation systems. We designed and implemented novel objective reduction SBSE methodologies to support scalable number of objectives. The proposed solutions were empirically evaluated in academic (open-source systems) and industrial settings.Ph.D.College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/138970/1/Dea Final Dissertation.pdfDescription of Dea Final Dissertation.pdf : DissertationDescription of Troh Josselin Dea Signed Certification Form.pdf : Committee signature fil

Interactive Optimisation in Marine Propeller Design

Marine propeller design is a complex engineering problem that depends on the collaboration of several scientific disciplines. During the design process, the blade designers need to consider contradicting requirements and come up with one optimal propeller design as a solution to the specific problem. This solution is usually the trade-o between the stakeholders\u27 requirements and the objectives and constraints of the problem.The significant amount of design variables related to blade design problems requires a systematic search in a large design space. Automated optimisation has been utilised for a number of blade design applications, as it has the advantage of creating a large set of design alternatives in a short period of time. However, automated optimisation has failed to be used in industrial applications, due to its complex set-up and the fact that in more complex scenarios the majority of the non-dominated design alternatives are infeasible. This necessitates a way of enabling the blade designers to interact with the algorithm during the optimisation process.The purpose of this thesis is to develop a methodology that supports the blade designers during the design process and to enable them to interact with the design tools and assess design characteristics during the optimisation. The overall aim is to improve the design performance and speed. According to the proposed methodology, blade designers are called during intermediate stages of the optimisation to provide information about the designs, and then this information is input in the algorithm. The goal is to steer the optimisation to an area of the design space with feasible Pareto designs, based on the designer\u27s preference. Since there are objectives and constraints that cannot be quantified with the available computational tools, keeping the "human in the loop" is essential, as a means to obtain feasible designs and quickly eliminate designs that are impractical or unrealistic.The results of this research suggest that through the proposed methodology the designers have more control over the whole optimisation procedure and they obtain detailed Pareto frontiers that involve designs that are characterised by high performance and follow the user preference

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

An Interactive Visualisation System for Engineering Design using Evolutionary Computing

This thesis describes a system designed to promote collaboration between the human and computer during engineering design tasks. Evolutionary algorithms (in particular the genetic algorithm) can find good solutions to engineering design problems in a small number of iterations, but a review of the interactive evolutionary computing literature reveals that users would benefit from understanding the design space and having the freedom to direct the search. The main objective of this research is to fulfil a dual requirement: the computer should generate data and analyse the design space to identify high performing regions in terms of the quality and robustness of solutions, while at the same time the user should be allowed to interact with the data and use their experience and the information provided to guide the search inside and outside regions already found. To achieve these goals a flexible user interface was developed that links and clarifies the research fields of evolutionary computing, interactive engineering design and multivariate visualisation. A number of accessible visualisation techniques were incorporated into the system. An innovative algorithm based on univariate kernel density estimation is introduced that quickly identifies the relevant clusters in the data from the point of view of the original design variables or a natural coordinate system such as the principal or independent components. The robustness of solutions inside a region can be investigated by novel use of 'negative' genetic algorithm search to find the worst case scenario. New high performance regions can be discovered in further runs of the evolutionary algorithm; penalty functions are used to avoid previously found regions. The clustering procedure was also successfully applied to multiobjective problems and used to force the genetic algorithm to find desired solutions in the trade-off between objectives. The system was evaluated by a small number of users who were asked to solve simulated engineering design scenarios by finding and comparing robust regions in artificial test functions. Empirical comparison with benchmark algorithms was inconclusive but it was shown that even a devoted hybrid algorithm needs help to solve a design task. A critical analysis of the feedback and results suggested modifications to the clustering algorithm and a more practical way to evaluate the robustness of solutions. The system was also shown to experienced engineers working on their real world problems, new solutions were found in pertinent regions of objective space; links to the artefact aided comparison of results. It was confirmed that in practice a lot of design knowledge is encoded into design problems but experienced engineers use subjective knowledge of the problem to make decisions and evaluate the robustness of solutions. So the full potential of the system was seen in its ability to support decision making by supplying a diverse range of alternative design options, thereby enabling knowledge discovery in a wide-ranging number of applications

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms