A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Strategy to Solve the Multistage Transmission Expansion Planning Problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Optimization Strategies Based on Sequential Quadratic Programming Applied for a Fermentation Process for Butanol Production

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

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.

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.

Short and long period optimization of drug doses in the treatment of AIDS

Técnicas de otimização numérica são úteis na solução de problemas de determinação da melhor entrada para sistemas descritos por modelos matemáticos e cujos objetivos podem ser expressos de uma maneira quantitativa. Este trabalho aborda o problema de otimizar as dosagens dos medicamentos no tratamento da AIDS em termos de um balanço entre a resposta terapêutica e os efeitos colaterais. Um modelo matemático para descrever a dinâmica do vírus HIV e células CD4 é utilizado para calcular a dosagem ótima do medicamento no tratamento a curto prazo de pacientes com AIDS por um método de otimização direta utilizando uma função custo do tipo Bolza. Os parâmetros do modelo foram ajustados com dados reais obtidos da literatura. Com o objetivo de simplificar os procedimentos numéricos, a lei de controle foi expressa em termos de uma expansão em séries que, após truncamento, permite obter controles sub-ótimos. Quando os pacientes atingem um estado clínico satisfatório, a técnica do Regulador Linear Quadrático (RLQ) é utilizada para determinar a dosagem permanente de longo período para os medicamentos. As dosagens calculadas utilizando a técnica RLQ , tendem a ser menores do que a equivalente terapia de dose constante em termos do expressivo aumento na contagem das células T+ CD4 e da redução da densidade de vírus livre durante um intervalo fixo de tempo.

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

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

Multiobjective optimization of economic balances of sugarcane harvest biomass

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

On the solution of the extended linear complementarity problem

The extended linear complementarity problem (XLCP) has been introduced in a recent paper by Mangasarian and Pang. In the present research, minimization problems with simple bounds associated to this problem are defined. When the XLCP is solvable, their solutions are global minimizers of the associated problems. Sufficient conditions that guarantee that stationary points of the associated problems are solutions of the XLCP will be proved. These theoretical results support the conjecture that local methods for box constrained optimization applied to the associated problems could be efficient tools for solving the XLCP. (C) 1998 Elsevier B.V. All rights reserved.

Aggregation in the generalized transportation problem

Aggregation disaggregation is used to reduce the analysis of a large generalized transportation problem to a smaller one. Bounds for the actual difference between the aggregated objective and the original optimal value are used to quantify the error due to aggregation and estimate the quality of the aggregation. The bounds can be calculated either before optimization of the aggregated problem (a priori) or after (a posteriori). Both types of the bounds are derived and numerically compared. A computational experiment was designed to (a) study the correlation between the bounds and the actual error and (b) quantify the difference of the error bounds from the actual error. The experiment shows a significant correlation between some a priori bounds, the a posteriori bounds and the actual error. These preliminary results indicate that calculating the a priori error bound is a useful strategy to select the appropriate aggregation level, since the a priori bound varies in the same way that the actual error does. After the aggregated problem has been selected and optimized, the a posteriori bound provides a good quantitative measure for the error due to aggregation.

Logarithmic barrier-augmented Lagrangian function to the optimal power flow problem

This paper presents a new approach to solve the Optimal Power Flow problem. This approach considers the application of logarithmic barrier method to voltage magnitude and tap-changing transformer variables and the other constraints are treated by augmented Lagrangian method. Numerical test results are presented, showing the effective performance of this algorithm. (C) 2005 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.

Multivariate analysis of photo-Fenton degradation of the herbicides tebuthiuron, diuron and 2,4-D

The degradation of herbicides in aqueous solution by photo-Fenton process using ferrioxalate complex (FeOx) as source of Fe2+ was evaluated under blacklight irradiation. The commercial products of the herbicides tebuthiuron, diuron and 2,4-D were used. The multivariate analysis, more precisely, the response surface methodology was applied to evaluate the role of FeOx and hydrogen peroxide concentrations as variables in the degradation process, and in particular, to define the concentration ranges that result in the most efficient degradation of the herbicides. The degradation process was evaluated by the determination of the remaining total organic carbon content (TOC), by monitoring the decrease of the concentrations of the original compounds using HPLC and by the chloride ion release in the case of diuron and 2,4-D. Under optimized conditions, 20min were sufficient to mineralize 93% of TOC from 2,4-D and 90% of diuron, including oxalate. Complete dechlorination of these compounds was achieved after 10 min reaction. It was found that the most recalcitrant herbicide is tebuthiuron, while diuron shows the highest degradability. However, under optimized conditions the initial concentration of tebuthiuron was reduced to less than 15%, while diuron and 2,4-D were reduced to around 2% after only 1 min reaction. Furthermore, it was observed that the ferrioxalate complex plays a more important role than H2O2 in the photodegradation of these herbicides in the ranges of concentrations investigated. (C) 2004 Elsevier Ltd. All rights reserved.

A new approach to the solution of the optimal power flow problem based on the modified Newton's method associated to an augmented Lagrangian function

This paper presents a new algorithm for optimal power flow problem. The algorithm is based on Newton's method which it works with an Augmented Lagrangian function associated with the original problem. The function aggregates all the equality and inequality constraints and is solved using the modified-Newton method. The test results have shown the effectiveness of the approach using the IEEE 30 and 638 bus systems.