Comparing classical generating methods with an evolutionary multi-objective optimization method

For the past decade, many evolutionary multi-objective optimization (EMO) methodologies have been developed and applied to find multiple Pareto-optimal solutions in a single simulation run. In this paper, we discuss three different classical generating methods, some of which were suggested even before the inception of EMO methodologies. These methods specialize in finding multiple Pareto-optimal solutions in a single simulation run. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, these algorithms are evaluated with an EMO methodology. The results bring out interesting insights about the strengths and weaknesses of these approaches. Further investigations of such classical generating methodologies and their evaluation should enable researchers to design a hybrid multi-objective optimization algorithm which may be better than each individual method

‘Optimulation’ in Chemical Reaction Engineering: The Oxidative Coupling of Methane as a Case Study

The optimization of reacting systems, including chemical, biological, and macromolecular reactions, is of great importance from both theoretical and practical standpoints. Even though several classical deterministic and stochastic modeling and simulation approaches have been routinely examined to understand and control reacting systems from lab- to industrial-scales, almost all tackling the same problem, i.e., how to predict reaction outputs from any given set of reaction input variables. Development and application of an effective and versatile mathematical tool capable of appropriately connecting preset desired reaction outputs to corresponding inputs have always been the ideal goal for experts in the related fields. Hence, there definitely exists the need to predict a priori optimum reaction conditions in a computationally-demanding multi-variable space for both keeping the chemical and biological reactions in optimal conditions and at the same time satisfying preset desired targets. As a novel and powerful solution, we hereby introduce a robust and functional computational tool capable of simultaneously simulating and optimizing, i.e. ‘optim-ulating’ intricate chemical, biological, and macromolecular reactions via the amalgamation of the Kinetic Monte Carlo (KMC) simulation approach and the multi-objective version of Genetic Algorithms (NSGA-II). The synergistic interplay of KMC and NSGA-II for the optimulation of Oxidative Coupling of Methane (OCM) as an example of a challenging chemical reaction engineering system has clearly demonstrated the outstanding capabilities of the proposed method. Undoubtedly, the proposed novel hybridized technique is very powerful and can address a variety of unsolved optimization questions in chemical, biological, and macromolecular reaction engineering

A Model for Performance Evaluation of Climate-Adaptive Building Envelopes Using Parametric Models and Multi-Criteria Optimization

The goal of this research is to enable designers to evaluate the performance of Climate-Adaptive Building Envelopes (CABE) to make better decisions at the conceptual design stage. This goal was accomplished by delivering three contributions to the fields of parametric modeling, building performance simulation, and multi-criteria optimization. There are three main challenges in CABE performance evaluation that cannot be overcome by conventional methods: 1) defining a suitable relationship between environmental factors and their thresholds by focusing on a given condition in CABE behavior control; 2) representing a CABE’s time-series behavior by using a single Building Performance Simulation (BPS) model; and 3) managing information related to a CABE’s performance and behavior for use in design decisions. To overcome these issues, this research developed a new CABE performance evaluation method called Parametric Behavior Maps (PBM), which makes three key contributions. First, the PBM method is able to generate a CABE operation schedule as an Hourly Behavior of Openness (HBOO) scenario to evaluate CABE performance using a single BPS model. Second, the PBM method produces more reliable outcomes than the conventional process, especially in terms of the time-lag effect of thermal performance. Third, the use of a Function-based Behavior Control System (FBCS) for the CABE efficiently facilitates a multi-criteria optimization process by progressively simulating alternative HBOO scenarios, allowing designers to choose the best scheme. These three contributions offer logical proof that the use of parametric modeling and simulation tools can help designers make better decisions regarding CABE alternatives. The PBM method was validated by investigating several test cases. First, static shading scenarios were developed using the PBM; the amount of incoming solar radiation was then compared with outcomes from the BPS with static shading. Second, indoor temperature profiles were simulated using the PBM method and an HBOO scenario; the results were compared with the outcomes obtained from the existing method, in order to determine the PBM’s reliability. Third, the integration of the PBM method and evolutionary multi-objective optimization technique illustrates the usefulness of the FBCS in CABE performance optimization

A Model for Performance Evaluation of Climate-Adaptive Building Envelopes Using Parametric Models and Multi-Criteria Optimization

The goal of this research is to enable designers to evaluate the performance of Climate-Adaptive Building Envelopes (CABE) to make better decisions at the conceptual design stage. This goal was accomplished by delivering three contributions to the fields of parametric modeling, building performance simulation, and multi-criteria optimization. There are three main challenges in CABE performance evaluation that cannot be overcome by conventional methods: 1) defining a suitable relationship between environmental factors and their thresholds by focusing on a given condition in CABE behavior control; 2) representing a CABE’s time-series behavior by using a single Building Performance Simulation (BPS) model; and 3) managing information related to a CABE’s performance and behavior for use in design decisions. To overcome these issues, this research developed a new CABE performance evaluation method called Parametric Behavior Maps (PBM), which makes three key contributions. First, the PBM method is able to generate a CABE operation schedule as an Hourly Behavior of Openness (HBOO) scenario to evaluate CABE performance using a single BPS model. Second, the PBM method produces more reliable outcomes than the conventional process, especially in terms of the time-lag effect of thermal performance. Third, the use of a Function-based Behavior Control System (FBCS) for the CABE efficiently facilitates a multi-criteria optimization process by progressively simulating alternative HBOO scenarios, allowing designers to choose the best scheme. These three contributions offer logical proof that the use of parametric modeling and simulation tools can help designers make better decisions regarding CABE alternatives. The PBM method was validated by investigating several test cases. First, static shading scenarios were developed using the PBM; the amount of incoming solar radiation was then compared with outcomes from the BPS with static shading. Second, indoor temperature profiles were simulated using the PBM method and an HBOO scenario; the results were compared with the outcomes obtained from the existing method, in order to determine the PBM’s reliability. Third, the integration of the PBM method and evolutionary multi-objective optimization technique illustrates the usefulness of the FBCS in CABE performance optimization

Optimization in design parameters of mechanical systems using multi-objective genetic algorithm

Orientador: Katia Lucchesi CavalcaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecanicaResumo: Os sistemas mecânicos são projetados para desempenhar funções específicas, e por essa razão as suas funções devem ser medidas para garantir seu desempenho dentro de uma certa precisão ou tolerância. A grande complexidade em se projetar e analisar novos projetos é a inserção de novas tecnologias, que envolvem aspectos multidisciplinares. Assim, o desenvolvimento e melhoria de projetos e produtos colocam o engenheiro projetista frente às diversas fontes de variabilidade, como por exemplo, as propriedades dos materiais, condições operacionais e ambientais e incertezas nas suposições feitas sobre seu funcionamento. Em termos de modelagem matemática, as aproximações inerentes e hipóteses feitas durante a concepção do sistema, conduzem normalmente a diferentes respostas obtidas através de simulações e/ou medidas experimentais. Dessa forma, em uma fase anterior à modelagem matemática,durante a concepção do sistema ou produto, as aplicações de ferramentas estatísticas e métodos de otimização podem fornecer estimativas sobre faixas de valores ou valores ótimos para parâmetros significativos de projeto, dentro do espaço experimental estudado. Esse tipo de abordagem estatística teve sua fundamentação teórica durante as décadas de 20 e 30 por Fisher, com a aplicação da teoria estatística sob diversos aspectos, como por exemplo: testes de hipóteses, estimativa de parâmetros, seleção de modelos, planejamento experimental e, mais tarde, no controle e melhoria de processos e produtos. Assim, este trabalho propõe um procedimento de estudo e otimização, integrando a teoria de planejamento experimental, a metodologia da superfície de resposta e otimização multi-objetivos através de algoritmos genéticos, para se obter a otimização dos parâmetros de projeto de componentes mecânicos. Em específico, foram utilizados dados de um sistema rotor-mancal e o estudo implica em minimizar as amplitudes no domínio da freqüência. Outro objetivo deste trabalho, foi desenvolver um programa para otimização multi-objetivos através de algoritmos genéticosAbstract: The mechanical systems are designed to be applied to any specific situations, and in this waytheir features should be measured to guarantee confidence to the systems. Their development and analysis expose the designer to a series of unknown parameters from several sources such as material properties, environmental and operational conditions. In terms of mathematical modeling, the inherent approximation and hypotheses made during system conception lead to different responses obtained by simulations and/or experimental measurements. So, in a previous phase of mathematical modeling, during the design analysis, the application of statistical tools and optimization methods is possible to estimate the values and/or ranges of the critical design parameters inside an experimental space. The connection between optimization and statistical data back at least to the early part of the 20th century and encompasses many aspects of applied and theoretical statistics, including hypothesis testing, parameter estimation, model selection, design of experiments and process and product control. So, this work proposes a link between theory of design of experiments, response surface methodology and multi-objective optimization using genetic algorithms, in order to optimize parameters for mechanical components. This study makes possible to verify the application of multi-objective optimization using genetic algorithms in design parameters and optimize them. A rotor-bearing system was used and amplitude in frequency domain was minimized. An experimental software for multi-objective optimization using genetic algorithm was developed.MestradoMecanica dos Sólidos e Projeto MecanicoMestre em Engenharia Mecânic

Accounting for water-, energy- and food-security impacts in developing country water infrastructure decision-making under uncertainty

Decision makers lack information and tools to help them understand non-revenue impacts of different water infrastructure investment and operation decisions on different stakeholders in developing countries. These challenges are compounded by multiple sources of uncertainty about the future, including climatic and socio-economic change. Many-objective trade-off analysis could improve understanding of the relationships between diverse stakeholder-defined benefits from a water resources system. It requires a river basin simulation model to evaluate the performance of the system resulting from different decisions. Metrics of performance can be defined in conjunction with stakeholders, relating the level of benefits they receive (monetised or otherwise) to flows or storages in the system. Coupling the model to a many-objective search algorithm allows billions of possible combinations of available decisions to be efficiently filtered to find those which maximise stakeholder benefits. Competition for water requires trade-offs, so a range of options can be generated which share resources differently. Uncertainties can be included in the analysis to help identify sets of decisions which provide acceptable benefits regardless of the future which manifests, i.e. perform robustly. From these options, decision makers can select a balance representing their preferences. This thesis reports the development of such a state-ofthe-art approach through applications in three real-world developing country contexts, with increasing levels of complexity and uncertainty. The first application in Brazil’s Jaguaribe Basin uses environmental and livelihoods indicators to help re-operate three existing dams. The second in Kenya’s Tana Basin adds new irrigation infrastructure investment options to decisions about re-operating a cascade of five existing dams in a more complex case. Finally robust portfolios of new hydropower investments are identified in Nepal’s Koshi Basin, accounting for climate and other uncertainties using a four-phased analytical approach. These applications confirm the approach’s utility and inform future research and practical use

Método NBI-EQMM com restrições multivariadas para otimização do processo de Torneamento Duro.

Esta tese apresenta o desenvolvimento e a avaliação do método NBI-EQMM com restrições multivariadas para problemas de otimização multiobjetivo não-linear de larga escala, com funções objetivo e restrições correlacionadas. No método, a seleção das funções que integram cada grupo é realizada aplicando-se a Análise Hierárquica de Cluster (AHC) assistida por uma matriz de distâncias. Para testar a adequação da proposta, um arranjo composto central (CCD) com 3 variáveis de entrada (x) e 22 respostas (Y) foi desenvolvido com vistas a otimizar o processo de torneamento do aço endurecido ABNT H13, usinado com as ferramentas Wiper PCBN 7025AWG, CC 6050WG e CC 650WG. As 22 superfícies de resposta foram definidas para que o problema pudesse contemplar cinco dimensões de um processo real, em escala industrial: qualidade, custo, produtividade, viabilidade econômica e financeira e sustentabilidade. Os resultados obtidos indicam que o método NBI-EQMM com restrições multivariadas de igualdade e desigualdade contribuiu para a formação de fronteiras equiespaçadas e sem inversão dos sinais de correlação das respostas originais, conduzindo todas as respostas para valores próximos aos seus alvos, sem desrespeitar as restrições multivariadas pré-estabelecidas. Foi observado que a inclusão das restrições multivariadas para o cálculo da matriz Payoff permite o reescalonamento da fronteira de Pareto, aproximando as soluções ótimas obtidas de seus ótimos individuais, evitando que soluções Pareto-ótimo fora da região de solução viável sejam obtidas. Observou-se ainda que, quando os eixos da fronteira de Pareto são formados por respostas positivamente correlacionadas e com o mesmo sentido de otimização ou negativamente correlacionadas e com o sentido de otimização diferente, o método NBI bivariado falha, corroborando, portanto, a necessidade de aplicação do método NBI-EQMM proposto. Considerando que um importante fator para a competitividade das organizações é a fabricação de produtos em grande escala, com custo mínimo e aliada a padrões de qualidade compatíveis aos exigidos pelos clientes, pode-se dizer que a ferramenta CC 6050WG conseguiu atender, simultaneamente, a todas essas características, sendo, portanto, considerada a mais eficiente entre as ferramentas analisadas nesta Tese