Um algoritmo baseado em evolução diferencial para problemas de otimização estrutural multiobjetivo com restrições

ResumoProblemas de otimização estrutural visam o aumento do desempenho da estrutura e a diminuição de seus custos garantindo, entretanto, os requisitos de segurança aplicáveis. Devido à natureza conflitante desses aspectos, a formulação de um problema de otimização estrutural como multiobjetivo é natural, embora pouco frequente, e tem a vantagem de apresentar um conjunto diversificado de soluções ao(s) tomador(es) de decisão. A literatura mostra que os algoritmos evolucionários (AE) são eficazes na obtenção de soluções em problemas de otimização multiobjetivo e que aqueles baseados em evolução diferencial (ED) são eficientes na resolução de problemas de otimização estrutural mono-objetivo, especialmente os que utilizam codificação real em suas variáveis de projeto. Por outro lado, nota-se a ausência da aplicação da ED na versão multiobjetivo desses problemas. Esse artigo apresenta uma análise do desempenho de um algoritmo baseado em ED em cinco exemplos de problemas de otimização estrutural multiobjetivo. Os resultados obtidos são comparados aos encontrados na literatura, indicando o potencial do algoritmo proposto.AbstractStructural optimization problems aim at increasing the performance of the structure while decreasing its costs guaranteeing, however, the applicable safety requirements. As these aspects are conflicting, the formulation of the structural optimization problem as multiobjective is natural but uncommon, and has the advantage of presenting a diverse set of solutions to the decision maker(s). The literature shows that Evolutionary Algorithms (EAs) are effective to obtain solutions in multiobjective optimization problems, and that the Differential Evolution (DE) based ones are efficient when solving structural mono-objective structural optimization problems, specially those with a real encoding of the design variables. On the other hand, one can note that DE has not yet been applied to the multiobjective version of these problems. This article presents a performance analysis of a DE-based algorithm in five multiobjective structural optimization problems. The obtained results are compared to those found in the literature, and the comparisons indicate the potential of the proposed algorithm

Which mechanism underlies the water-like anomalies in core-softened potentials?

Using molecular dynamics simulations we investigate the thermodynamic of particles interacting with a continuous and a discrete versions of a core-softened (CS) intermolecular potential composed by a repulsive shoulder. Dynamic and structural properties are also analyzed by the simulations. We show that in the continuous version of the CS potential the density at constant pressure has a maximum for a certain temperature. Similarly the diffusion constant, $D$, at a constant temperature has a maximum at a density $\rho_{\mathrm{max}}$ and a minimum at a density $\rho_{\mathrm{min}}<\rho_{\mathrm{max}}$, and structural properties are also anomalous. For the discrete CS potential none of these anomalies are observed. The absence of anomalies in the discrete case and its presence in the continuous CS potential are discussed in the framework of the excess entropy.Comment: 8 page

An artificial fish swarm filter-based Method for constrained global optimization

Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions. The preliminary numerical experiments with a wellknown benchmark set of engineering design problems show the effectiveness of the proposed method.Fundação para a Ciência e a Tecnologia (FCT

Multilocal programming and applications

Preprint versionMultilocal programming aims to identify all local minimizers of unconstrained or constrained nonlinear optimization problems. The multilocal programming theory relies on global optimization strategies combined with simple ideas that are inspired in deflection or stretching techniques to avoid convergence to the already detected local minimizers. The most used methods to solve this type of problems are based on stochastic procedures and a population of solutions. In general, population-based methods are computationally expensive but rather reliable in identifying all local solutions. In this chapter, a review on recent techniques for multilocal programming is presented. Some real-world multilocal programming problems based on chemical engineering process design applications are described.Fundação para a Ciência e a Tecnologia (FCT

A Ga-simplex Hybrid Algorithm For Global Minimization Of Molecular Potential Energy Functions

In this paper we propose a hybrid genetic algorithm for minimizing molecular potential energy functions. Experimental evidence shows that the global minimum of the potential energy of a molecule corresponds to its most stable conformation, which dictates its properties. The search for the global minimum of a potential energy function is very difficult since the number of local minima grows exponentially with molecule size. The proposed approach was successfully applied to two cases: (i) a simplified version of more general molecular potential energy functions in problems with up to 100 degrees of freedom, and (ii) a realistic potential energy function modeling two different molecules. © 2005 Springer Science + Business Media, Inc.1381189202Davis, L., (1991) Handbook of Genetic Algorithms, , Van Nostrand ReinholdEshelman, L.J., Schaffer, J.D., Real coded genetic algorithms and interval schemata (1993) Foundations of Genetic Algorithms, 2. , D. Whitley (ed.), San Mateo, CA: Morgan KaufmannFloudas, C.A., Klepeis, J.L., Pardalos, P.M., Global optimization approaches in protein folding and peptide docking (1999) DIMACS Series in Discrete Mathematics and Theoretical Computer Science, , American Mathematical SocietyHarp, S.A., Samad, T., Guha, A., Towards the genetic synthesis of neural networks (1989) Proc. of the Third Int. Conf. on Genetic Algorithms and Their Applications, , J.D. Schaffer (ed.), San Mateo, CA: Morgan KaufmannHolland, J.H., (1975) Adaptation in Natural and Artificial Systems, , University of Michigan Press, Ann ArborLavor, C., Maculan, N., A function to test methods applied to global minimization of potential energy of molecules (2004) Numerical Algorithms, 35, pp. 287-300Maranas, C.D., Floudas, C.A., Global minimum potential energy conformations of small molecules (1994) J. Global. Opt., 4, pp. 135-170Maranas, C.D., Floudas, C.A., A deterministic global optimization approach for molecular structure determination (1994) J. Chem. Phys., 100, pp. 1247-1261Mathias, K., Staged hybrid genetic search for seismic data imaging (1994) Proc. of IEEE World Congress on Evolutionary Computation, , D. Fogel and Z. Michalewicz (eds.), Piscataway, NJ, USAMichalewicz, Z., (1992) Genetic Algorithms + Data Structures = Evolution Programs, , New York: Springer-VerlagMichalewicz, Z., Schoenauer, M., Evolutionary algorithms for constrained parameter optimization problems (1996) Evolutionary Computation, 4, pp. 1-32Nelder, J.A., Mead, R., A simplex method for function minimization (1965) Computer Journal, 7, pp. 308-313Pardalos, P.M., Shalloway, D., Xue, G.L., Optimization methods for computing global minima of nonconvex potential energy functions (1994) J. Global Optim., 4, pp. 117-133Press, W.H., (1992) Numerical Recipes, Second Edition, , Cambridge University PressRadcliffe, N.J., Surry, P.D., (1994) Formal Memetic Algorithms, Evolutionary Computing: AISB Workshop, , T.C. Fogarty (ed.), LNCS 865. Springer-VerlagSmith, S., The simplex method and evolutionary algorithms (1998) Proc. of the 5th Int. Conf. on Evolutionary Computation, , Anchorage, AlaskaSpendley, W., Hext, G.R., Himsworth, F.R., Sequential application of simplex designs in optimization and evolutionary operation (1962) Technometrics, 4, pp. 441-461Wales, D.J., Scheraga, H.A., Global optimization of clusters, crystals and biomolecules (1999) Science, 285, pp. 1368-1372Whitley, D., The GENITOR algorithm and selective pressure (1989) Proc. of the Third Int. Conf. on Genetic Algorithms and Their Applications, , J.D. Schaffer (ed.), CA: Morgan Kaufmann, San MateoYen, J., A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method (1998) IEEE Transactions on Systems, Man. and Cybernetics - Part B: Cybernetics, 28, pp. 1205-121