Algoritmo evolutivo paralelo para o problema de atribuição de localidades a anéis em redes sonet/sdh

The telecommunications play a fundamental role in the contemporary society, having as one of its main roles to give people the possibility to connect them and integrate them into society in which they operate and, therewith, accelerate development through knowledge. But as new technologies are introduced on the market, increases the demand for new products and services that depend on the infrastructure offered, making the problems of planning of telecommunication networks become increasingly large and complex. Many of these problems, however, can be formulated as combinatorial optimization models, and the use of heuristic algorithms can help solve these issues in the planning phase. This paper proposes the development of a Parallel Evolutionary Algorithm to be applied to telecommunications problem known in the literature as SONET Ring Assignment Problem SRAP. This problem is the class NP-hard and arises during the physical planning of a telecommunication network and consists of determining the connections between locations (customers), satisfying a series of constrains of the lowest possible cost. Experimental results illustrate the effectiveness of the Evolutionary Algorithm parallel, over other methods, to obtain solutions that are either optimal or very close to it

Um algoritmo híbrido para o problema de roteamento de veículos com frotas heterogêneas

This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.

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)

Metodologia estatística na solução do problema do caixeiro viajante e na avaliação de algoritmos : um estudo aplicado à transgenética computacional

The problems of combinatory optimization have involved a large number of researchers in search of approximative solutions for them, since it is generally accepted that they are unsolvable in polynomial time. Initially, these solutions were focused on heuristics. Currently, metaheuristics are used more for this task, especially those based on evolutionary algorithms. The two main contributions of this work are: the creation of what is called an -Operon- heuristic, for the construction of the information chains necessary for the implementation of transgenetic (evolutionary) algorithms, mainly using statistical methodology - the Cluster Analysis and the Principal Component Analysis; and the utilization of statistical analyses that are adequate for the evaluation of the performance of the algorithms that are developed to solve these problems. The aim of the Operon is to construct good quality dynamic information chains to promote an -intelligent- search in the space of solutions. The Traveling Salesman Problem (TSP) is intended for applications based on a transgenetic algorithmic known as ProtoG. A strategy is also proposed for the renovation of part of the chromosome population indicated by adopting a minimum limit in the coefficient of variation of the adequation function of the individuals, with calculations based on the population. Statistical methodology is used for the evaluation of the performance of four algorithms, as follows: the proposed ProtoG, two memetic algorithms and a Simulated Annealing algorithm. Three performance analyses of these algorithms are proposed. The first is accomplished through the Logistic Regression, based on the probability of finding an optimal solution for a TSP instance by the algorithm being tested. The second is accomplished through Survival Analysis, based on a probability of the time observed for its execution until an optimal solution is achieved. The third is accomplished by means of a non-parametric Analysis of Variance, considering the Percent Error of the Solution (PES) obtained by the percentage in which the solution found exceeds the best solution available in the literature. Six experiments have been conducted applied to sixty-one instances of Euclidean TSP with sizes of up to 1,655 cities. The first two experiments deal with the adjustments of four parameters used in the ProtoG algorithm in an attempt to improve its performance. The last four have been undertaken to evaluate the performance of the ProtoG in comparison to the three algorithms adopted. For these sixty-one instances, it has been concluded on the grounds of statistical tests that there is evidence that the ProtoG performs better than these three algorithms in fifty instances. In addition, for the thirty-six instances considered in the last three trials in which the performance of the algorithms was evaluated through PES, it was observed that the PES average obtained with the ProtoG was less than 1% in almost half of these instances, having reached the greatest average for one instance of 1,173 cities, with an PES average equal to 3.52%. Therefore, the ProtoG can be considered a competitive algorithm for solving the TSP, since it is not rare in the literature find PESs averages greater than 10% to be reported for instances of this size.

Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

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

Study of the potentiometric response of the doped spinel Li1.05Al0.02Mn1.98O4 for the optimization of a selective lithium ion sensor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

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

Search schemes for random optimization algorithms that preserve the asymptotic distribution

Markovian algorithms for estimating the global maximum or minimum of real valued functions defined on some domain Omega subset of R-d are presented. Conditions on the search schemes that preserve the asymptotic distribution are derived. Global and local search schemes satisfying these conditions are analysed and shown to yield sharper confidence intervals when compared to the i.i.d. case.

The open capacitated arc routing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Topliss method in the optimization of salicylic acid derivatives as potential antimycobacterial agents

The Topliss method was used to guide a synthetic path in support of drug discovery efforts toward the identification of potent antimycobacterial agents. Salicylic acid and its derivatives, p-chloro, p-methoxy, and m-chlorosalicylic acid, exemplify a series of synthetic compounds whose minimum inhibitory concentrations for a strain of Mycobacterium were determined and compared to those of the reference drug, p-aminosalicylic acid. Several physicochemical descriptors (including Hammett's sigma constant, ionization constant, dipole moment, Hansch constant, calculated partition coefficient, Sterimol-L and -B-4 and molecular volume) were considered to elucidate structure-activity relationships. Molecular electrostatic potential and molecular dipole moment maps were also calculated using the AM1 semi-empirical method. Among the new derivatives, m-chlorosalicylic acid showed the lowest minimum inhibitory concentration. The overall results suggest that both physicochemical properties and electronic features may influence the biological activity of this series of antimycobacterial agents and thus should be considered in designing new p-aminosalicylic acid analogs.

Cyclodextrin glycosyltransferase from Bacillus licheniformis: Optimization of production and its properties

Cyclodextrin glycosyltransferase (EC 2.4.1.19) is an enzyme that produces cyclodextrins from starch via an intramolecular transglycosylation reaction. An alkalophilic Bacillus strain, isolated from cassava peels, was identified as Bacillus licheniformis. CGTase production by this strain was better when potato starch was used as carbon source, followed by cassava starch and amylopectin. Glucose and amylose, on the other hand, acted as synthesis repressors. When the cultivation was supplemented with sodium ions and had the pH adjusted between 6.0 and 9.0, the microorganism maintained the growth and enzyme production capacity. This data is interesting because it contradicts the concept that alkalophilic microorganisms do not grow in this pH range. After ultrafiltration-centrifugation, one protein of 85.2 kDa with CGTase activity was isolated. This protein was identified in plates with starch and phenolphthalein. Determination of the optimum temperature showed higher activities at 25 degrees C and 55 degrees C, indicating the possible presence of more than one CGTase in the culture filtrate. Km and Vmax values were 1.77 mg/mL and 0.0263 U/mg protein, respectively, using potato starch as substrate.

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.