910 resultados para Multi-objective Optimization (MOO)
Resumo:
Piezoresistive materials, materials whose resistivity properties change when subjected to mechanical stresses, are widely utilized in many industries as sensors, including pressure sensors, accelerometers, inclinometers, and load cells. Basic piezoresistive sensors consist of piezoresistive devices bonded to a flexible structure, such as a cantilever or a membrane, where the flexible structure transmits pressure, force, or inertial force due to acceleration, thereby causing a stress that changes the resistivity of the piezoresistive devices. By applying a voltage to a piezoresistive device, its resistivity can be measured and correlated with the amplitude of an applied pressure or force. The performance of a piezoresistive sensor is closely related to the design of its flexible structure. In this research, we propose a generic topology optimization formulation for the design of piezoresistive sensors where the primary aim is high response. First, the concept of topology optimization is briefly discussed. Next, design requirements are clarified, and corresponding objective functions and the optimization problem are formulated. An optimization algorithm is constructed based on these formulations. Finally, several design examples of piezoresistive sensors are presented to confirm the usefulness of the proposed method.
Resumo:
Here, we study the stable integration of real time optimization (RTO) with model predictive control (MPC) in a three layer structure. The intermediate layer is a quadratic programming whose objective is to compute reachable targets to the MPC layer that lie at the minimum distance to the optimum set points that are produced by the RTO layer. The lower layer is an infinite horizon MPC with guaranteed stability with additional constraints that force the feasibility and convergence of the target calculation layer. It is also considered the case in which there is polytopic uncertainty in the steady state model considered in the target calculation. The dynamic part of the MPC model is also considered unknown but it is assumed to be represented by one of the models of a discrete set of models. The efficiency of the methods presented here is illustrated with the simulation of a low order system. (C) 2010 Elsevier Ltd. All rights reserved.
Resumo:
This paper studies a simplified methodology to integrate the real time optimization (RTO) of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. Optimal conditions of the process at steady state are searched through the use of a rigorous non-linear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model, obtained through a plant step test. The main advantage of the proposed strategy is that the resulting control/optimization problem can still be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed may be comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a non-linear programming problem with a much higher computer load. (C) 2010 Elsevier Ltd. All rights reserved.
Resumo:
There is an increasing need to treat effluents contaminated with phenol with advanced oxidation processes (AOPs) to minimize their impact on the environment as well as on bacteriological populations of other wastewater treatment systems. One of the most promising AOPs is the Fenton process that relies on the Fenton reaction. Nevertheless, there are no systematic studies on Fenton reactor networks. The objective of this paper is to develop a strategy for the optimal synthesis of Fenton reactor networks. The strategy is based on a superstructure optimization approach that is represented as a mixed integer non-linear programming (MINLP) model. Network superstructures with multiple Fenton reactors are optimized with the objective of minimizing the sum of capital, operation and depreciation costs of the effluent treatment system. The optimal solutions obtained provide the reactor volumes and network configuration, as well as the quantities of the reactants used in the Fenton process. Examples based on a case study show that multi-reactor networks yield decrease of up to 45% in overall costs for the treatment plant. (C) 2010 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
Resumo:
The aim objective of this project was to evaluate the protein extraction of soybean flour in dairy whey, by the multivariate statistical method with 2(3) experiments. Influence of three variables were considered: temperature, pH and percentage of sodium chloride against the process specific variable ( percentage of protein extraction). It was observed that, during the protein extraction against time and temperature, the treatments at 80 degrees C for 2h presented great values of total protein (5.99%). The increasing for the percentage of protein extraction was major according to the heating time. Therefore, the maximum point from the function that represents the protein extraction was analysed by factorial experiment 2(3). By the results, it was noted that all the variables were important to extraction. After the statistical analyses, was observed that the parameters as pH, temperature, and percentage of sodium chloride, did not sufficient for the extraction process, since did not possible to obtain the inflection point from mathematical function, however, by the other hand, the mathematical model was significant, as well as, predictive.
Resumo:
Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.
Resumo:
Background: Oncologic outcomes in men with radiation-recurrent prostate cancer (PCa) treated with salvage radical prostatectomy (SRP) are poorly defined. Objective: To identify predictors of biochemical recurrence (BCR), metastasis, and death following SRP to help select patients who may benefit from SRP. Design, setting, and participants: This is a retrospective, international, multi-institutional cohort analysis. There was amedian follow-up of 4.4 yr following SRP performed on 404 men with radiation-recurrent PCa from 1985 to 2009 in tertiary centers. Intervention: Open SRP. Measurements: BCR after SRP was defined as a serum prostate-specific antigen (PSA) >= 0.1 or >= 0.2 ng/ml (depending on the institution). Secondary end points included progression to metastasis and cancerspecific death. Results and limitations: Median age at SRP was 65 yr of age, and median pre-SRP PSA was 4.5 ng/ml. Following SRP, 195 patients experienced BCR, 64 developed metastases, and 40 died from PCa. At 10 yr after SRP, BCR-free survival, metastasis-free survival, and cancer-specific survival (CSS) probabilities were 37% (95% confidence interval [CI], 31-43), 77% (95% CI, 71-82), and 83% (95% CI, 76-88), respectively. On preoperative multivariable analysis, pre-SRP PSA and Gleason score at postradiation prostate biopsy predicted BCR (p = 0.022; global p < 0.001) and metastasis (p = 0.022; global p < 0.001). On postoperative multivariable analysis, pre-SRP PSA and pathologic Gleason score at SRP predicted BCR (p = 0.014; global p < 0.001) and metastasis (p < 0.001; global p < 0.001). Lymph node involvement (LNI) also predicted metastasis (p = 0.017). The main limitations of this study are its retrospective design and the follow-up period. Conclusions: In a select group of patients who underwent SRP for radiation-recurrent PCa, freedom from clinical metastasis was observed in > 75% of patients 10 yr after surgery. Patients with lower pre-SRP PSA levels and lower postradiation prostate biopsy Gleason score have the highest probability of cure from SRP. (C) 2011 European Association of Urology. Published by Elsevier B. V. All rights reserved.
Resumo:
Combinatorial optimization problems share an interesting property with spin glass systems in that their state spaces can exhibit ultrametric structure. We use sampling methods to analyse the error surfaces of feedforward multi-layer perceptron neural networks learning encoder problems. The third order statistics of these points of attraction are examined and found to be arranged in a highly ultrametric way. This is a unique result for a finite, continuous parameter space. The implications of this result are discussed.
Resumo:
Carpooling initiated in America in the 1970s due to the oil crisis. However, over the past years, carpooling has increased significantly across the world. Some countries have created a High Occupancy Vehicle (HOV) lane to encourage commuters not to travel alone. In additional, carpool websites has been developed to facilitate the connection between the commuters, making it possible to create a compatible match in a faster and efficient manner. This project focuses on carpooling, especially in an academic environment since younger people are more likely to choose carpool. Initially, an intense research was made to examine carpool studies that occurred all over the world, following with a research of higher education institutes that use carpooling as a transportation mode. Most websites created carpools by targeting people from a specific country. These commuters have different origins and destinations making it more complicated to create compatible matches. The objective of this project is to develop a system helping teachers and students from an academic environment to create carpool matches. This objective makes it easier to create carpools because these students and teachers have the same destination. During the research, it was essential to explore, as many as possible, existing carpool websites that are available across the world. After this analysis, several sketches were made to develop the layout and structure of the web application that’s being implemented throughout the project. Once the layout was established, the development of the web application was initiated. This project had its ups and downs but it accomplished all the necessary requirements. This project can be accessed on the link: http://ipcacarpool.somee.com. Once the website was up and running, a web-based survey was developed to study the reasons that motivate people to consider carpooling as an alternative to driving alone. To develop this survey was used a tool called Survey Planet. This survey contained 408 respondents, which 391 are students and 17 are teachers. This study concludes that a majority of the respondents don’t carpool, however they will consider carpooling if there was a dedicated parking space. A majority of the respondents that carpool initiated less than a year ago, indicating that this mean of transportation is recent.
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A energia eléctrica é um bem essencial para a maioria das sociedades. O seu fornecimento tem sido encarado como um serviço público, da responsabilidade dos governos, através de empresas monopolistas, públicas e privadas. O Mercado Ibérico de Electricidade (MIBEL) surge com o objectivo da integração e cooperação do sector eléctrico Português e Espanhol, no qual é possível negociar preços e volumes de energia. Actualmente, as entidades podem negociar através de um mercado bolsista ou num mercado de contratos bilaterais. Uma análise dos mercados de electricidade existentes mostra que estes estão longe de estarem liberalizados. As tarifas não reflectem o efeito da competitividade. Além disso, o recurso a contratos bilaterais limita frequentemente os clientes a um único fornecedor de energia eléctrica. Nos últimos anos, têm surgido uma série de ferramentas computacionais que permitem simular, parte ou a totalidade, dos mercados de electricidade. Contudo, apesar das suas potencialidades, muitos simuladores carecem de flexibilidade e generalidade. Nesta perspectiva, esta dissertação tem como principal objectivo o desenvolvimento de um simulador de mercados de energia eléctrica que possibilite lidar com as dificuldades inerentes a este novo modelo de mercado, recorrendo a agentes computacionais autónomos. A dissertação descreve o desenho e a implementação de um simulador simplificado para negociação de contratos bilaterais em mercados de energia, com particular incidência para o desenho das estratégias a utilizar pelas partes negociais. Além disso, efectua-se a descrição de um caso prático, com dados do MIBEL. Descrevem-se também várias simulações computacionais, envolvendo retalhistas e consumidores de energia eléctrica, que utilizam diferentes estratégias negociais. Efectua-se a análise detalhada dos resultados obtidos. De forma sucinta, os resultados permitem concluir que as melhores estratégias para cada entidade, no caso prático estudado, são: a estratégia de concessões fixas, para o retalhista, e a estratégia de concessões baseada no volume de energia, para o consumidor.
Resumo:
Topology optimization consists in finding the spatial distribution of a given total volume of material for the resulting structure to have some optimal property, for instance, maximization of structural stiffness or maximization of the fundamental eigenfrequency. In this paper a Genetic Algorithm (GA) employing a representation method based on trees is developed to generate initial feasible individuals that remain feasible upon crossover and mutation and as such do not require any repairing operator to ensure feasibility. Several application examples are studied involving the topology optimization of structures where the objective functions is the maximization of the stiffness and the maximization of the first and the second eigenfrequencies of a plate, all cases having a prescribed material volume constraint.
Resumo:
This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.
Resumo:
Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.
Resumo:
This paper presents a Swarm based Cooperation Mechanism for scheduling optimization. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to support decision making in agile manufacturing environments. Agents coordinate their actions automatically without human supervision considering a common objective – global scheduling solution taking advantages from collective behavior of species through implicit and explicit cooperation. The performance of the cooperation mechanism will be evaluated consider implicit cooperation at first stage through ACS, PSO and ABC algorithms and explicit through cooperation mechanism application.
Resumo:
Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).