A Competitive Comparison of Different Types of Evolutionary Algorithms

Hrstka, Ondřej; Kučerová, Anna; Lepš, M.; Zeman, Jan

doi:10.4203/ccp.74.37

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…In the worst case, the reliability of the algorithm is 76% due to very small number of objective functions calls set in the termination condition. From our experience with evolutionary algorithms [7], such a result is rather satisfactory. iv) As the result of a sequential character of the identification procedure, the errors in identified parameters accumulate.…”

Section: Resultsmentioning

confidence: 90%

Novel anisotropic continuum‐discrete damage model capable of representing localized failure of massive structures

Brancherie

Ibrahimbegović²

2009

Engineering Computations

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In Part I of this paper we have presented a simple model capable of describing the localized failure of a massive structure. In this part, we discuss the identification of the model parameters from two kinds of experiments: a uniaxial tensile test and a three-point bending test. The former is used only for illustration of material parameter response dependence, and we focus mostly upon the latter, discussing the inverse optimization problem for which the specimen is subjected to a heterogeneous stress field.

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Section: Resultsmentioning

confidence: 90%

Novel anisotropic continuum‐discrete damage model capable of representing localized failure of massive structures

Brancherie

Ibrahimbegović²

2009

Engineering Computations

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“…The optimal values of the four free parameters of the model were found using the genetic algorithm GRADE which was successfully applied in many engineering tasks before [18]- [19]. The scheme of the optimization process is given in Fig.6.…”

Section: Methods Of Computational Modelingmentioning

confidence: 99%

Determination of Radiative Heat Transfer Coefficient at High Temperatures Using a Combined Experimental-Computational Technique

Kočí

Korecký

et al. 2015

Measurement Science Review

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The radiative heat transfer coefficient at high temperatures is determined using a combination of experimental measurement and computational modeling. In the experimental part, cement mortar specimen is heated in a laboratory furnace to 600°C and the temperature field inside is recorded using built-in K-type thermocouples connected to a data logger. The measured temperatures are then used as input parameters in the three dimensional computational modeling whose objective is to find the best correlation between the measured and calculated data via four free parameters, namely the thermal conductivity of the specimen, effective thermal conductivity of thermal insulation, and heat transfer coefficients at normal and high temperatures. The optimization procedure which is performed using the genetic algorithms provides the value of the high-temperature radiative heat transfer coefficient of 3.64 W/(m 2 K).

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“…Recently, a variety of RC applications has been discussed in the literature. Examples include the work of Hrstka et al [12] and Leps and Sejnoha [13], who optimized several types of RC beams; Lee and…”

Section: Introductionmentioning

confidence: 99%

Heuristic optimization of RC bridge piers with rectangular hollow sections

Martínez

González-Vidosa

Hospitaler

et al. 2010

Computers & Structures

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ElsevierMartínez Martín, FJ.; Hospitaler Pérez, A.; Yepes Piqueras, V. (2010). Heuristic optimization of RC bridge piers with rectangular hollow sections. Computers and Structures. 88:375-386. doi:10.1016/j.compstruc.2009.11 Keywords: structural design, economic optimization, ant colony optimization, concrete structures. 3 IntroductionThe design of bridge piers is crucial for the design of prestressed concrete viaducts. The piers make up between 20% and 50% of the total cost of the viaduct depending on pier heights and foundation conditions. Rectangular hollow cross-sections as described in the present paper are most frequently used. Current designs of such reinforced concrete (RC) structures are highly conditioned by the experience of structural engineers. Design procedures usually adopt cross-section dimensions and material grades based on commonly sanctioned practice. Once the geometry and materials of the structure are specified, the reinforcement of the pier is tentatively defined according to experience. The first-order stress resultants are analyzed and second-order (buckling) stress resultants are then estimated according to simplified and conservative formulae or following a more general method that accounts for second-order deformations and includes the non-linear stiffness of the column. Tentative passive reinforcement must then satisfy the limit states prescribed by concrete codes. Should the dimensions, the material grades or the reinforcement be insufficient, the structure is redefined on a trial-and-error basis. This process leads to safe designs, but the cost of the RC pier is, consequently, highly dependent upon the experience of the structural designer. In contrast to designs based on experience, artificial intelligence has been applied to a variety of fields including the solution of constrained problems. The design of RC structures is a problem of selecting design variables as subject to structural constraints for which artificial intelligence is aptly suited. These methods involve simple algorithms, but they also require a considerable computational effort, since they include a large number of iterations in which the objective function is evaluated and the structural constraints are checked. The rectangular hollow section piers object of this study are those commonly used in the construction of cast-in-place prestressed concrete road and railway viaducts. The objective of this study is to examine the heuristic optimization of this type of RC structure. The method followed consisted in developing an evaluation computer module in which cross-section dimensions, materials and steel reinforcement are taken as discrete variables. This module computes the cost of a solution and checks all the relevant limit states. ACO, GA and TA algorithms are then used to search the solution space. It is important to note that the present study is an updated and revised version of the conference 6 study by Martinez et al. [21], which was an initial analysis that concentrated on ACO algorithms and did not includ...

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A Competitive Comparison of Different Types of Evolutionary Algorithms

Cited by 11 publications

References 7 publications

Novel anisotropic continuum‐discrete damage model capable of representing localized failure of massive structures

Novel anisotropic continuum‐discrete damage model capable of representing localized failure of massive structures

Determination of Radiative Heat Transfer Coefficient at High Temperatures Using a Combined Experimental-Computational Technique

Heuristic optimization of RC bridge piers with rectangular hollow sections

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