Optimal trajectory and solution of the inverse kinematics of a robotic manipulator by genetic algorithms

This paper presents the generation of optimal trajectories by genetic algorithms (GA) for a planar robotic manipulator. The implemented GA considers a multi-objective function that minimizes the end-effector positioning error together with the joints angular displacement and it solves the inverse kinematics problem for the trajectory. Computer simulations results are presented to illustrate this implementation and show the efficiency of the used methodology producing soft trajectories with low computing cost. © 2011 Springer-Verlag Berlin Heidelberg.

A meta-heurística de busca dispersa aplicada no planejamento da expansão de sistemas de transmissão

Pós-graduação em Engenharia Elétrica - FEIS

A common Tabu search algorithm for the global optimization of engineering problems

A novel common Tabu algorithm for global optimizations of engineering problems is presented. The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving a practical engineering problem. The numerical results show that the proposed method is (i) superior to the conventional Tabu search algorithm in robustness, and (ii) superior to the simulated annealing algorithm in efficiency. (C) 2001 Elsevier B.V. B.V. All rights reserved.

Optimization of fibrolytic enzyme production by Aspergillus japonicus C03 with potential application in ruminant feed and their effects on tropical forages hydrolysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Lactic acid production by new Lactobacillus plantarum LMISM6 grown in molasses: optimization of medium composition

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Efficient robust optimization based on polynomial chaos and tabu search algorithm

The study of robust design methodologies and techniques has become a new topical area in design optimizations in nearly all engineering and applied science disciplines in the last 10 years due to inevitable and unavoidable imprecision or uncertainty which is existed in real word design problems. To develop a fast optimizer for robust designs, a methodology based on polynomial chaos and tabu search algorithm is proposed. In the methodology, the polynomial chaos is employed as a stochastic response surface model of the objective function to efficiently evaluate the robust performance parameter while a mechanism to assign expected fitness only to promising solutions is introduced in tabu search algorithm to minimize the requirement for determining robust metrics of intermediate solutions. The proposed methodology is applied to the robust design of a practical inverse problem with satisfactory results.

Synthesis and optimization of colloidal silica nanoparticles and their functionalization with methacrylic acid

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Design and management optimization of trickle irrigation systems using non-linear programming

A non-linear model is presented which optimizes the lay-out, as well as the design and management of trickle irrigation systems, to achieve maximum net benefit. The model consists of an objective function that maximizes profit at the farm level, subject to appropriate geometric and hydraulic constraints. It can be applied to rectangular shaped fields, with uniform or zero slope. The software used is the Gams-Minos package. The basic inputs are the crop-water-production function, the cost function and cost of system components, and design variables. The main outputs are the annual net benefit and pipe diameters and lengths. To illustrate the capability of the model, a sensitivity analysis of the annual net benefit for a citrus field is evaluated with respect to irrigated area, ground slope, micro-sprinkler discharge and shape of the field. The sensitivity analysis suggests that the greatest benefit is obtained with the smallest microsprinkler discharge, the greatest area, a square field and zero ground slope. The costs of the investment and energy are the components of the objective function that had the greatest effect in the 120 situations evaluated. (C) 1996 Academic Press Limited

Modelling and optimization of the cephalosporin C production bioprocess in a fed-batch bioreactor with invert sugar as substrate

Cephalosporin C production process optimization was studied based on four experiments carried out in an agitated and aerated tank fermenter operated as a fed-batch reactor. The microorganism Cephalosporium acremonium ATCC 48272 (C-10) was cultivated in a synthetic medium containing glucose as major carbon and energy source. The additional medium contained a hydrolyzed sucrose solution as the main carbon and energy source and it was added after the glucose depletion. By manipulating the supplementary feed rate, it was possible to increase antibiotic production. A mathematical model to represent the fed-batch production process was developed. It was observed that the model was applicable under different operation conditions, showing that optimization studies can be made based on this model. (C) 1999 Elsevier B.V. Ltd. All rights reserved.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimization of the Transesterification Reaction in Biodiesel Production and Determination of Density and Viscosity of Biodiesel/Diesel Blends at Several Temperatures

Waste frying oil has been used to optimize the production of biodiesel. Biodiesel was prepared through sodium ethoxide catalyzed methanolysis from the transesterification of recycled waste frying oil. Optimization of the transesterification reaction for biodiesel production was carried out by means of statistical analyses using ANOVA. The optimum conditions for reaction were the following: a oil methanol mole ratio of 1:9, temperature of 50 degrees C, catalyst mass fraction of 0.9 %, and reaction time of 40 min, which enabled a yield of 98.7 % determined by gas chromatography/mass spectrometry (GC/MS) analysis. The density and viscosity of biodiesel/diesel blends have been determined as a function of composition at several temperatures.

A linear optimization approach to the combined production planning model

Two fundamental processes usually arise in the production planning of many industries. The first one consists of deciding how many final products of each type have to be produced in each period of a planning horizon, the well-known lot sizing problem. The other process consists of cutting raw materials in stock in order to produce smaller parts used in the assembly of final products, the well-studied cutting stock problem. In this paper the decision variables of these two problems are dependent of each other in order to obtain a global optimum solution. Setups that are typically present in lot sizing problems are relaxed together with integer frequencies of cutting patterns in the cutting problem. Therefore, a large scale linear optimizations problem arises, which is exactly solved by a column generated technique. It is worth noting that this new combined problem still takes the trade-off between storage costs (for final products and the parts) and trim losses (in the cutting process). We present some sets of computational tests, analyzed over three different scenarios. These results show that, by combining the problems and using an exact method, it is possible to obtain significant gains when compared to the usual industrial practice, which solve them in sequence. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.