A new evolutionary operation called the complicial method is presented. The main criterion, which is adhered to, is that changes in the independent variables are restricted to a small step-size from a previous best trial. The complicial method is essentially a modification of the simplicial method proposed by Spendley, Hext and Himsworth in which these authors employ regular type arrays in a sequential type search for the optimum. The complicial method differs from the simplicial method in that an irregular array is formed when (and only when) the last trial is proven to be the best of those previously tested. The design of this irregular array is such that a regular array can be formed when the last trial is proven not to be the best so far. The complicial method is compared to the simplicial method for a wide variety of response surfaces in both the absence and presence of random error. It is found that the complicial method is much more effective (i.e. the relative effectiveness is very large) for almost all the test response surfaces involving a small number of variables. Although an increase in the amount of random error decreases the effectiveness of both methods, the relative effectiveness generally remains unchanged. However, as the number of variables is increased the relative effectiveness is found to decrease markedly. This is explained by considerations of the basic design of the regular and irregular arrays. Because the complicial method sacrifices some of the simplicity characteristic of the simplicial method, it is recommended that the complicial method be applied only in situations where the relative effectiveness is very large. Therefore, this method is best used for all types of response surfaces involving a small number, of variables. / Applied Science, Faculty of / Chemical and Biological Engineering, Department of / Graduate

The practical implementation of optimal control systems in large industrial process applications has been limited by the high costs of the required computing facilities. With the recent advances in component fabrication and the resultant decrease in hardware costs, special purpose computers, utilizing virtually no software at all, can be constructed as economical alternatives to presently available general purpose computers for use in optimal process controllers. A design for one such special purpose machine, an automatic optimizer, is presented in this thesis. Tests conducted on a working optimizer constructed on the basis of the given design, demonstrate that it is suitably fast and powerful for use in process controllers. In addition, the optimizer is inexpensive enough to be used as part of an economical process controller. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

The use of multiobjective optimization in identifying systems that account for changes in customer needs, operating environments, system design concepts, and analysis models over time is generally not explored. Providing solutions that anticipate, account for, and allow for these changes over time is a significant challenge to manufacturers and design engineers. Products that adapt to these changes through the addition and/or subtraction of modules can reduce production costs through product commonality, and cater to customization and adaptation. In terms of identifying sets of non-dominated designs, these changes result in the concept of dynamic Pareto frontiers, or dynamic s-Pareto frontiers when sets of system concepts are simultaneously evaluated over time. In this dissertation, a five-step optimization-based design method identifying a set of optimal adaptive product designs that satisfy the predicted changes by moving from one location on the dynamic s-Pareto frontier to another through the addition of a module and/or through reconfiguration is developed. Development of this five-step method was separated into four phases. The first two phases of developments respectively focus on Pareto and s-Pareto cases, where changes in concepts, models, and environments that would effect the Pareto/s-Pareto frontier are ignored due to limitations in traditional optimization problem formulations. To overcome these limitations, and allow for these changes, the third phase of developments presents a generic optimization formulation capable of identifying a dynamic s-Pareto frontier, while the fourth phase adapts the phase three method to incorporate this new dynamic optimization formulation. Example implementations of the four phases of developments were respectively provided through the design of a modular UAV, a hurricane and flood resistant modular residential structure, a simple aircraft design example inspired by the Lockheed C-130 Hercules, and a modular truss system. Noting that modular products only represent one approach for dealing with changes in preferences, environments, models, and concepts, the final research contribution connects the presented method with parallel research developments in collaborative product design and design principles identification, followed by two case study implementations of this unifying design approach in the development of a modular irrigation pump and a modular plywood cart for developing countries.

Several techniques were studied to select and prioritize technologies for a complex system. Based on the findings, a method called Pareto Optimization and Selection of Technologies (POST) was formulated to efficiently explore the combinatorial technology space. A knapsack problem was selected as a benchmark problem to test-run various algorithms and techniques of POST. A Monte Carlo simulation using the surrogate models was used for uncertainty quantification. The concepts of graph theory were used to model and analyze compatibility constraints among technologies. A probabilistic Pareto optimization, based on the concepts of Strength Pareto Evolutionary Algorithm II (SPEA2), was formulated for Pareto optimization in an uncertain objective space. As a result, multiple Pareto hyper-surfaces were obtained in a multi-dimensional objective space; each hyper-surface representing a specific probability level. These Pareto layers enabled the probabilistic comparison of various non-dominated technology combinations. POST was implemented on a technology exploration problem for a 300 passenger commercial aircraft. The problem had 29 identified technologies with uncertainties in their impacts on the system. The distributions for these uncertainties were defined using beta distributions. Surrogate system models in the form of Response Surface Equations (RSE) were used to map the technology impacts on the system responses. Computational complexity of technology graph was evaluated and it was decided to use evolutionary algorithm for probabilistic Pareto optimization. The dimensionality of the objective space was reduced using a dominance structure preserving approach. Probabilistic Pareto optimization was implemented with reduced number of objectives. Most of the technologies were found to be active on the Pareto layers. These layers were exported to a dynamic visualization environment enabled by a statistical analysis and visualization software called JMP. The technology combinations on these Pareto layers are explored using various visualization tools and one combination is selected. The main outcome of this research is a method based on consistent analytical foundation to create a dynamic tradeoff environment in which decision makers can interactively explore and select technology combinations.

Submitted by Leonardo Cavalcante (leo.ocavalcante@gmail.com) on 2018-06-11T11:34:10Z No. of bitstreams: 1 Arquivototal.pdf: 1995596 bytes, checksum: bfcc1e1f3a77514dcbf7a8e4f5e4701b (MD5) / Made available in DSpace on 2018-06-11T11:34:10Z (GMT). No. of bitstreams: 1 Arquivototal.pdf: 1995596 bytes, checksum: bfcc1e1f3a77514dcbf7a8e4f5e4701b (MD5) Previous issue date: 2017-02-16 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The problem in investment portfolio selection consists in the allocation of resources to a finite number of assets, aiming, in its classic approach, to overcome a trade-off between the risk and expected return of the portfolio. This problem is one of the most important topics targeted at today’s financial and economic issues. Since the pioneering works of Markowitz, the issue is treated as an optimisation problem with the two aforementioned objectives. However, in recent years, various restrictions and additional risk measurements were identified in the literature, such as, for example, cardinality restrictions, minimum transaction lot and asset pre-selection. This practice aims to bring the issue closer to the reality encountered in financial markets. In that regard, this paper proposes a metaheuristic called Particle Swarm for the optimisation of several PSPs, in such a way that allows the resolution of the problem considering a set of restrictions chosen by the investor. / O problema de seleção de carteiras de investimentos (PSP) consiste na alocação de recursos a um número finito de ativos, objetivando, em sua abordagem clássica, superar um trade-off entre o retorno esperado e o risco da carteira. Tal problema ´e uma das temáticas mais importantes voltadas a questões financeiras e econômicas da atualidade. Desde os pioneiros trabalhos de Markowitz, o assunto é tratado como um problema de otimização com esses dois objetivos citados. Entretanto, nos últimos anos, diversas restrições e mensurações de riscos adicionais foram consideradas na literatura, como, por exemplo, restrições de cardinalidade, de lote mínimo de transação e de pré-seleção de ativos. Tal prática visa aproximar o problema da realidade encontrada nos mercados financeiros. Neste contexto, o presente trabalho propõe uma meta-heurística denominada Adaptive Non-dominated Sorting Multiobjective Particle Swarm Optimization para a otimização de vários problemas envolvendo PSP, de modo que permita a resolução do problema considerando um conjunto de restri¸c˜oes escolhidas pelo investidor.