Improved global–local simulated annealing formulation for solving non-smooth engineering optimization problems

Genovese, Katia; Lamberti, Luciano; Pappalettere, Carmine

doi:10.1016/j.ijsolstr.2004.07.015

Cited by 38 publications

(41 citation statements)

References 14 publications

(20 reference statements)

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“…In order to solve this lack, some modifications have been proposed, such as an increase in the probability of sample points far from the present point [63] or the use of a set of points at a time instead of only one [64]. In order to increase the rate of convergence, Genovese et al [65] proposed a two-level algorithm, including a "global annealing" in which all the design variables were disrupted simultaneously and a "local annealing" in which only one design variable was disrupted at a time. The local annealing was performed after each iteration of the global annealing in an attempt to improve the testing point locally.…”

Section: Simulated Annealingmentioning

confidence: 99%

Memetic Computing Applied to the Design of Composite Materials and Structures

Peláez

Gómez-Ruiz

Veintimilla

et al. 2017

Mathematical Problems in Engineering

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Presently, there exists an important need for lighter and more resistant structures, with reduced manufacturing costs. Laminated polymers are materials which respond to these new demands. Main difficulties of the design process of a composite laminate include the necessity to design both the geometry of the element and the material configuration itself and, therefore, the possibilities of creating composite materials are almost unlimited. Many techniques, ranging from linear programming or finite elements to computational intelligence, have been used to solve this type of problems. The aim of this work is to show that more effective and dynamic methods to solve this type of problems are obtained by using certain techniques based on systematic exploitation of knowledge of the problem, together with the combination of metaheuristics based on population as well as on local search. With this objective, a memetic algorithm has been designed and compared with the main heuristics used in the design of laminated polymers in different scenarios. All solutions obtained have been validated by the ANSYS5 software package.

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Section: Simulated Annealingmentioning

confidence: 99%

Memetic Computing Applied to the Design of Composite Materials and Structures

Peláez

Gómez-Ruiz

Veintimilla

et al. 2017

Mathematical Problems in Engineering

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“…The formulation of Eq. (1) or its normalized version, which we call the basic least-squares in the rest of the paper, have been recently used, for example, in the domain of elastic constants identification from full field strain measurements [5][6][7][8] or from vibration data [9][10][11][12].…”

Section: A Least-squares Formulationsmentioning

confidence: 99%

“…In spite of the existence of advanced least-squares formulations, which take into account statistical information [2][3][4], the simplest formulations of the leastsquares method, based on minimizing the L 2 norm of the residual [2], are still extensively used today [5][6][7][8][9][10][11][12]. This basic nonstatistical leastsquares formulation is very simple and most often leads to reasonably accurate results.…”

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confidence: 99%

Introduction to the Bayesian Approach Applied to Elastic Constants Identification

et al. 2010

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The basic formulation of the least-squares method, based on the L 2 norm of the residuals, is still widely used today for identifying elastic constants of aerospace materials from experimental data. While this method often works well, methods that can benefit from statistical information, such as the Bayesian method, may sometimes be more accurate. We seek situations with significant difference between the material properties identified by the two methods. For a simple three-bar truss example we illustrate three situations in which the Bayesian approach systematically leads to more accurate results: different sensitivity of the measured response to the parameters to be identified, different uncertainty in the measurements, and correlation among response components. When all three effects add up, the Bayesian approach can be much more accurate. Furthermore, the Bayesian approach has the additional advantage of providing the uncertainty in the identified parameters. We also compare the two methods for a more realistic problem of identification of elastic constants from natural frequencies of a composite plate.

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“…One more popular algorithm for stochastic optimization (Baumann 2008;Calafiore and Dabbene 2008) is Simulated Annealing (Lamberti and Pappalettere 2007;Lamberti 2008;Genovese et. al.…”

Section: Choice Of Optimization Algorithmmentioning

confidence: 99%

Global Optimization of Grillages Using Simulated Annealing and High Performance Computing

Šešok

Mockus²,

Belevičius

et al. 2010

Journal of Civil Engineering and Management

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Abstract. The aim is to investigate ways of increasing the efficiency of grillage optimization. Following this general aim, two well-known optimization methods, namely the Genetic Algorithm (GA) and Simulated Annealing (SA), were compared using some standard medium size (10 and 15 piles) examples. The objective function was the maximal vertical reactive force at a support. Coordinates of piles were optimization variables. SA wins and was applied to real-life problem (55 piles) by parallel computations performed using a powerful cluster. New element is comparison of SA with GA and application of SA to a practical problem of grillage optimization.

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Improved global–local simulated annealing formulation for solving non-smooth engineering optimization problems

Cited by 38 publications

References 14 publications

Memetic Computing Applied to the Design of Composite Materials and Structures

Memetic Computing Applied to the Design of Composite Materials and Structures

Introduction to the Bayesian Approach Applied to Elastic Constants Identification

Global Optimization of Grillages Using Simulated Annealing and High Performance Computing

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