Global optimization using a genetic algorithm with hierarchically structured population

This paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.

Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.

A smooth evolutionary structural optimization procedure applied to plane stress problem

Topological optimization problems based on stress criteria are solved using two techniques in this paper. The first technique is the conventional Evolutionary Structural Optimization (ESO), which is known as hard kill, because the material is discretely removed; that is, the elements under low stress that are being inefficiently utilized have their constitutive matrix has suddenly reduced. The second technique, proposed in a previous paper, is a variant of the ESO procedure and is called Smooth ESO (SESO), which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it no longer influences the structure; its removal is thus performed smoothly. This procedure is known as "soft-kill"; that is, not all of the elements removed from the structure using the ESO criterion are discarded. Thus, the elements returned to the structure must provide a good conditioning system that will be resolved in the next iteration, and they are considered important to the optimization process. To evaluate elasticity problems numerically, finite element analysis is applied, but instead of using conventional quadrilateral finite elements, a plane-stress triangular finite element was implemented with high-order modes for solving complex geometric problems. A number of typical examples demonstrate that the proposed approach is effective for solving problems of bi-dimensional elasticity. (C) 2014 Elsevier Ltd. All rights reserved.

Generalized interval vector spaces and interval optimization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Search for neutral Higgs bosons decaying to tau pairs in pp collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for Neutral Minimal Supersymmetric Standard Model Higgs Bosons Decaying to Tau Pairs in pp Collisions at root s=7 TeV

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluating the use of predatory insects as bioindicators of metals contamination due to sugarcane cultivation in neotropical streams

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

Bioaccumulation of metals in aquatic insects of streams located in areas with sugar cane cultivation

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

Lattice discretization in quantum scattering

The utility of lattice discretization technique is demonstrated for solving nonrelativistic quantum scattering problems and specially for the treatment of ultraviolet divergences in these problems with some potentials singular at the origin in two- and three-space dimensions. This shows that the lattice discretization technique could be a useful tool for the numerical solution of scattering problems in general. The approach is illustrated in the case of the Dirac delta function potential.

LOW-ENERGY BEHAVIOR OF FEW-PARTICLE SCATTERING-AMPLITUDES IN 2 DIMENSIONS

A study of the analytic behavior of different few-particle scattering amplitudes at low energies in two space dimensions is presented. Such a study is of use in modeling and understanding different few-particle processes at low energies. A detailed discussion of the energy and the momentum dependence of the partial-wave on-the-energy-shell and off-the-energy-shell two-particle t matrices is given. These t-matrix elements tend to zero as the energy and momentum variables tend to zero. The multiple-scattering series is used to show that the connected three-to-three amplitudes diverge in the low-energy-momentum limit. Unitarity relations are used to show that the connected two-to-three and one-to-three amplitudes have specific logarithmic singularities at the m-particle breakup threshold. The subenergy singularity in the two-to-three amplitudes is also studied, and comments are made on some applications of the present study in different problems of ph cal interest.

Renormalization in non-relativistic quantum mechanics

The importance and usefulness of renormalization are emphasized in non-relativistic quantum mechanics. The momentum space treatment of both two-body bound state and scattering problems involving some potentials singular at the origin exhibits ultraviolet divergence. The use of renormalization techniques in these problems leads to finite converged results for both the exact and perturbative solutions. The renormalization procedure is carried out for the quantum two-body problem in different partial waves for a minimal potential possessing only the threshold behaviour and no form factors. The renormalized perturbative and exact solutions for this problem are found to be consistent with each other. The useful role of the renormalization group equations for this problem is also pointed out.

H2 and/or H∞-norm model reduction of uncertain discrete-time systems

This paper addresses the problem of model reduction for uncertain discrete-time systems with convex bounded (polytope type) uncertainty. A reduced order precisely known model is obtained in such a way that the H2 and/or the H∞ guaranteed norm of the error between the original (uncertain) system and the reduced one is minimized. The optimization problems are formulated in terms of coupled (non-convex) LMIs - Linear Matrix Inequalities, being solved through iterative algorithms. Examples illustrate the results.

The use of the two-body energy to study problems of escape/capture Ernesto Vieira Neto1

The problem of escape/capture is encountered in many problems of the celestial mechanics -the capture of the giants planets irregular satellites, comets capture by Jupiter, and also orbital transfer between two celestial bodies as Earth and Moon. To study these problems we introduce an approach which is based on the numerical integration of a grid of initial conditions. The two-body energy of the particle relative to a celestial body defines the escape/capture. The trajectories are integrated into the past from initial conditions with negative two-body energy. The energy change from negative to positive is considered as an escape. By reversing the time, this escape turns into a capture. Using this technique we can understand many characteristics of the problem, as the maximum capture time, stable regions where the particles cannot escape from, and others. The advantage of this kind of approach is that it can be used out of plane (that is, for any inclination), and with perturbations in the dynamics of the n-body problem. © 2005 International Astronomical Union.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Una metodología eficiente para manejo de restricciones en algoritmos evolutivos multi-objetivo

This paper presents a new approach for solving constraint optimization problems (COP) based on the philosophy of lexicographical goal programming. A two-phase methodology for solving COP using a multi-objective strategy is used. In the first phase, the objective function is completely disregarded and the entire search effort is directed towards finding a single feasible solution. In the second phase, the problem is treated as a bi-objective optimization problem, turning the constraint optimization into a two-objective optimization. The two resulting objectives are the original objective function and the constraint violation degree. In the first phase a methodology based on progressive hardening of soft constraints is proposed in order to find feasible solutions. The performance of the proposed methodology was tested on 11 well-known benchmark functions.