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Journal articles on the topic 'Evolutionary structural optimization method'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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1

Hu, Xing Guo, and He Ming Cheng. "Truss Optimization Based on the Evolutionary Structural Optimization." Advanced Materials Research 915-916 (April 2014): 281–84. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.281.

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The Evolutionary Structural Optimization (ESO) as an important structural topology optimization method has been widely used in many fields of engineering optimization. However, due to some technical constraints, the use of ESO for the truss optimization is relatively less. A method for truss optimization that combines the ESO method and the Stress Ratio method is proposed in this paper. This method solves the problems of ESO for truss optimization that the sectional area of bars cannot be changed and the speed of optimization cannot be easily controlled. It can be widely used in truss optimization and can get the same good result as other methods (such as GA and SA, etc.). Furthermore, the method proposed in this paper has the advantage that it can be easily programmed in the commercial software (such as Ansys and Abaqus, etc.) owing to its relatively simple optimization principle.
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2

Li, Ying Di, Bing Kuang, and Juan Liu. "SIMP-Based Evolutionary Structural Optimization Method for Topology Optimization." Applied Mechanics and Materials 651-653 (September 2014): 2237–40. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2237.

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Unreasonable parameters may lead to a phenomenon of the numerical instability in evolutionary structural optimization method (ESO). In this paper the improved SIMP-based ESO method for the structural compliance sensitivity is presented to solve the problem of checkerboard pattern. The method depends on the sensitivity analysis results that indicate the contribution of each unit for the whole structural performance to delete and to add elements. At the same time, the method in combination with a sensitivity redistribution technology of controlling checkerboard pattern is used to realize that each element’s contribution or impact factor of the whole structural performance has a smooth transition. The instance shows that the method is reasonable and different parameters will affect the optimized results. The optimal values of parameters can be seen obviously finally.
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3

Tanskanen, Pasi. "The evolutionary structural optimization method: theoretical aspects." Computer Methods in Applied Mechanics and Engineering 191, no. 47-48 (2002): 5485–98. http://dx.doi.org/10.1016/s0045-7825(02)00464-4.

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4

Jia, Haipeng, H. G. Beom, Yuxin Wang, Song Lin, and Bo Liu. "Evolutionary level set method for structural topology optimization." Computers & Structures 89, no. 5-6 (2011): 445–54. http://dx.doi.org/10.1016/j.compstruc.2010.11.003.

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5

Chen, Lu Yun. "Structural-Acoustic Topology Analysis Based on Evolutionary Structural Optimization." Applied Mechanics and Materials 575 (June 2014): 343–49. http://dx.doi.org/10.4028/www.scientific.net/amm.575.343.

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Application of the evolutionary structural optimization (ESO) approach for structural acoustic optimization is investigated. Combining the sizing optimization, a more appropriate rejection criterion for ESO was put forward. There is a trial of applying modified evolutionary structural optimization (MESO) method in dynamic response problem. Finally, the topology optimization of plate structure under harmonic loading as example, the MESO approach is conducted. The numerical results show that the MESO is feasible in topology optimization analysis; and it expands the application of traditional ESO theory.
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6

Azamirad, Ghasem, and Behrooz Arezoo. "Topology optimization of stamping die components using evolutionary structural optimization method." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 4 (2016): 690–98. http://dx.doi.org/10.1177/0954405415597630.

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Today, die design standards are used to design the structure of die components. These standards are usually based on high safety factors. So, the die components are often heavier and larger than required. In this article, a software package is developed which can design an appropriate topology of body structure of stamping die components with a reduced weight. This is done by implementing the evolutionary structural optimization algorithm. The proposed structure can also be modified by the designer to accommodate for a simpler casting method. This software package is developed in Microsoft Visual C# programming environment with a link to Abaqus software to analyze the finite element simulation of the process. The operation of the software is demonstrated by an example where the die for a sheet metal part is studied. The die components are initially designed, analyzed and compared with the standard die (the die which is in general use today). The final results show a reduction of 37% of volume and 8% of maximum displacement, respectively.
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Xia, Liang, Li Zhang, Qi Xia, and Tielin Shi. "Stress-based topology optimization using bi-directional evolutionary structural optimization method." Computer Methods in Applied Mechanics and Engineering 333 (May 2018): 356–70. http://dx.doi.org/10.1016/j.cma.2018.01.035.

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8

Glebov, A. O., and A. I. Skomorokhova. "Evolutionary structural optimization of vulcanization molds." Journal of Physics: Conference Series 2388, no. 1 (2022): 012100. http://dx.doi.org/10.1088/1742-6596/2388/1/012100.

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Abstract A method of evolutionary topology optimization of vulcanization molds according to the criteria of the maximum temperature deviation in the volume of the rubber mixture from the set value and the mechanical compliance of the structure is proposed. To take into account the transient of the mold heating process, the condition of specific heat absorption in the volume of the processed rubber mixture is proposed. The problem of topology optimization of a mold for the manufacture of a rubber diaphragm has been solved. The analysis of the received results from the point of view of efficiency of the decision and manufacturability of a design is carried out. An engineering interpretation of the results of topology optimization has been carried out.
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9

Han, Seog-Young. "An improved element removal method for evolutionary structural optimization." KSME International Journal 14, no. 9 (2000): 913–19. http://dx.doi.org/10.1007/bf03185793.

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10

He, Yuan Chao, Wen Lin Chen, and Li Na Hao. "Topological Optimization for Damping Structure of Cylindrical Shell Based on DIMFM." Applied Mechanics and Materials 472 (January 2014): 152–56. http://dx.doi.org/10.4028/www.scientific.net/amm.472.152.

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On the basis of evolutionary structural optimization, the topology optimization for free layer damping cylindrical shell was studied. The checkerboard pattern problem caused by evolutionary structural optimization was solved by discrete independent mesh filter method. The programs of evolutionary structural optimization and discrete independent mesh filter method were made in ANSYS. The optimal topology configuration of free layer damping cylindrical shell was obtained and the effectiveness of Discrete Independent Mesh Filter Method was validated.
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11

Xu, Jian, Jian Yuan Sun, and Yu Long Shui. "Bi-Directional Evolutionary Structural Optimization in Conceptual Bridge Design." Applied Mechanics and Materials 256-259 (December 2012): 1658–64. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1658.

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The principle and procedure of bi-directional evolutionary structural optimization (BESO) are stated in detail. A program based on BESO is introduced in conceptual bridge design. Topology optimizations are achieved for deck arch bridges with different rise-to-span ratios, half-through arch bridge, through tied arch bridge, bridge pier and bridge main beam. The results demonstrate rational structures with well-distributed stress and smooth force transmission, which indicates the efficient of the method.
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12

Choi, S. H., J. Y. Park, I. S. Shin, and Seok Young Han. "Topology Optimization of a Vehicle’s Hood Using Evolutionary Structural Optimization." Key Engineering Materials 326-328 (December 2006): 1217–20. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.1217.

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Topology optimization of the inner reinforcement for a vehicle’s hood has been performed by evolutionary structural optimization (ESO) method. The purpose of this study is to obtain optimal topology of the inner reinforcement for a vehicle’s hood considering static stiffness and natural frequency simultaneously. To do this, the multiobjective design optimization technique was implemented. From several combinations of weighting factors, a Pareto-optimal solution was obtained. Optimal topologies were obtained by the ESO method, i.e., by eliminating the elements having the lowest efficiency from the structural domain. As the weighting factor of the elastic strain efficiency goes from 1 to zero, it is found that the optimal topologies transmits from the optimal topology of static stiffness problem to that of natural frequency problem. Therefore, it was concluded that ESO method is effectively applied to topology optimization of the inner reinforcement of a vehicle’s hood.
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13

Wang, Bing Qin, Bing Li Wang, and Zhi Yuan Huang. "Topology Optimization for Constrained Layer Damping Plates Using Evolutionary Structural Optimization Method." Advanced Materials Research 894 (February 2014): 158–62. http://dx.doi.org/10.4028/www.scientific.net/amr.894.158.

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The evolutionary structural optimization (ESO) is used to optimize constrained damping layer structure. Considering the vibration and energy dissipation mode of the plate with constrained layer damping treatment, the elements of constrained damping layers and modal loss factor are considered as design variable and objective function, while damping material consumption is considered as a constraint. The sensitivity of modal loss factor to design variable is further derived using modal strain energy analysis method. Numerical example is used to demonstrate the effectiveness of the proposed topology optimization approach. The results show that vibration energy dissipation of the plates can be enhanced by the optimal constrained layer damping layout.
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14

Sun, X. F., J. Yang, Y. M. Xie, X. Huang, and Z. H. Zuo. "Topology Optimization of Composite Structure Using Bi-Directional Evolutionary Structural Optimization Method." Procedia Engineering 14 (2011): 2980–85. http://dx.doi.org/10.1016/j.proeng.2011.07.375.

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15

Hu, Xing Guo, and He Ming Cheng. "Evolutionary Structural Optimization Using Material Efficiency Grades." Applied Mechanics and Materials 482 (December 2013): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amm.482.317.

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In the evolutionary structural optimization (ESO) using the rejection ratio, the criterion of the inefficient material removal cant generally be lowered during the optimization process because the rejection ratio cant be decreased. Owing to this, some sorts of structures in special load cases cannot be optimized smoothly; or the late optimization of an ordinary structure is terminated suddenly when the removal of material abruptly increases excessively. The paper puts forward the Evolutionary Structural Optimization using Material Efficiency Grades (ESO-MEG) in order to eliminate the unfavorable effects of the rejection ratio on the results of ESO. The ESO-MEG can determine inefficient material in a structural optimization according to the efficiency grades of each part of material, so it can adjust timely and flexibly the criterion of inefficient material removal. The research shows that the ESO-MEG is applicable to the optimization of different sorts of structures in varying sorts of load cases, so generalization of this method has a broad prospect.
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16

Zhu, Jialei, Xiaodong Liang, Shuai Shao, Yongdong Bi, and Runze Miao. "Improved Bi-Direction Evolutionary Structural Optimization Method and Its Application." IOP Conference Series: Earth and Environmental Science 632 (January 14, 2021): 042032. http://dx.doi.org/10.1088/1755-1315/632/4/042032.

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17

Chung, Tien Tung, Jia Pei Wang, Yan Zuo Chen, and Ta Chuan Liu. "Bi-Directional Evolutionary Structural Optimization Method with Draw Direction Constraints." Applied Mechanics and Materials 420 (September 2013): 346–51. http://dx.doi.org/10.4028/www.scientific.net/amm.420.346.

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This paper proposes a new bi-directional evolutionary structural optimization (BESO) method with draw direction constraints. Draw direction constraints, defined by required manufacturing process, are achieved by modifying element removal/addition criteria such that elements are removed from the top surface of the draw direction to the inner design domain. The optimized design with draw direction constraints is free from hollow or closed cavity geometries which are infeasible for manufacturing. A stiffness design of a motor front cover is carried out to show the ability of the proposed method in practical mechanical design problems.
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18

Chen, Xiao Ming, Xi De Lai, Xiang Zhang, Wei Song, and Zhen Lu. "Improvement of Evolutionary Structural Optimization Method for 2-D Model." Applied Mechanics and Materials 380-384 (August 2013): 1409–13. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1409.

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Which the distortion elements appear in the optimization process and the result may be a partial optimum solution are two disadvantages when using the ESO method to optimize 2-D model. In this paper, two algorithms are proposed for solving these disadvantages. One is filtering and deleting algorithm based on the traits of distortion elements. Using this algorithm, only one parameter is needed to control filtering and deleting, and the parameter is determined by the number of elements linked around distortion elements. The other is interval approximation algorithm. Minimum initial rejection ratio RR0min which fulfills removal criterion was ascertained by using this algorithm and it was used as starting value. Thus other initial erasure rate RR0 were obtained by setting a certain increment. Two algorithms mentioned above were implanted into the existing ESO method. Then optimizing respectively with different initial removal rates which were obtained before and the available structures from the results were filtered out in terms of performance parameters. The results of optimization for 2-D structures indicate that the improved ESO method has better suitability and stability than the existing one, and it avoids shortcoming that the results may be a partial optimum solution when using the existing ESO method.
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19

Wang, Hongxin, Jie Liu, and Guilin Wen. "An efficient evolutionary structural optimization method for multi-resolution designs." Structural and Multidisciplinary Optimization 62, no. 2 (2020): 787–803. http://dx.doi.org/10.1007/s00158-020-02536-0.

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20

Xie, Y. M., P. Felicetti, J. W. Tang, and M. C. Burry. "Form finding for complex structures using evolutionary structural optimization method." Design Studies 26, no. 1 (2005): 55–72. http://dx.doi.org/10.1016/j.destud.2004.04.001.

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21

El Hami, Norelislam, Mhamed Itmi, and Abdelkhalak El Hami. "Hybrid Evolutionary Optimization Algorithm for Structures." Advanced Materials Research 1099 (April 2015): 102–9. http://dx.doi.org/10.4028/www.scientific.net/amr.1099.102.

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In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem. In the case of optimization of structure, the objective is to play a dominant role in the structural optimization problem introducing the reliability concept. The optimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states. In this paper we use a new optimization method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Numerical results show the robustness of the MPSO-SA algorithm.
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22

Hu, Jie, Song Yao, Ning Gan, Yulin Xiong, and Xing Chen. "Fracture strength topology optimization of structural specific position using a bi-directional evolutionary structural optimization method." Engineering Optimization 52, no. 4 (2019): 583–602. http://dx.doi.org/10.1080/0305215x.2019.1609466.

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23

Azamirad, Ghasem, and Behrooz Arezoo. "Structural design of stamping die components using bi-directional evolutionary structural optimization method." International Journal of Advanced Manufacturing Technology 87, no. 1-4 (2016): 969–79. http://dx.doi.org/10.1007/s00170-016-8344-7.

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24

ZUO, Z. H., Y. M. XIE, and X. HUANG. "AN IMPROVED BI-DIRECTIONAL EVOLUTIONARY TOPOLOGY OPTIMIZATION METHOD FOR FREQUENCIES." International Journal of Structural Stability and Dynamics 10, no. 01 (2010): 55–75. http://dx.doi.org/10.1142/s0219455410003415.

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This paper presents a topology optimization method for dynamic problems with an improved bi-directional evolutionary structural optimization (BESO) technique. The sensitivity derivation for the frequency optimization problem in the case of multiple eigenvalues and for the stiffness–frequency optimization problem is proposed. Algorithms for a filter scheme, sensitivity history-averaging, and sensitivity global-ranking are used in the present method. Techniques for adaptively removing alternative elements and eliminating singular and single-hinged elements are proposed. Solution-convergence and localized modes are discussed through numerical examples. Results show that the improved BESO method is capable of solving the frequency optimization problem and the multi-objective optimization problem for stiffness and frequency effectively.
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25

Kopeliovich, M. V., M. V. Petrushan, and A. I. Samarin. "Evolutionary algorithm for structural-parametric optimization of the remote photoplethysmography method." Optical Memory and Neural Networks 26, no. 1 (2017): 55–61. http://dx.doi.org/10.3103/s1060992x17010052.

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26

Qiu, Wenke, Shaomeng Jin, Liang Xia, and Tielin Shi. "Length scale control schemes for bi‐directional evolutionary structural optimization method." International Journal for Numerical Methods in Engineering 123, no. 3 (2021): 755–73. http://dx.doi.org/10.1002/nme.6874.

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27

Patil, Sachin, Shi Wei Zhou, and Qing Li. "Design of Periodic Microstructural Materials by Using Evolutionary Structural Optimization Method." Advanced Materials Research 32 (February 2008): 279–83. http://dx.doi.org/10.4028/www.scientific.net/amr.32.279.

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Despite significant success in developing various periodic composites, the challenge remains how to more efficiently design the base cell so that one or more physical properties can be attained. In this paper, the material design problem is formulated in a form of the least square of the difference between the targeted and designed values. By minimizing the objective subject to volume constraints and periodic boundary conditions, an optimal material distribution in base cell can be generated. Different from existing methods, this paper shows how to use the Evolutionary Structural Optimization (ESO) method to design composite material attaining to thermal conductivity defined by the Hashin-Strikman (H-S) bounds. The effectiveness of this method is demonstrated through several 2D examples, agreeing well with commonly known benchmarking microstructures.
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HAN, Seog-Young, and Soo-Kyoung LEE. "Development of a Material Mixing Method Based on Evolutionary Structural Optimization." JSME International Journal Series A 48, no. 3 (2005): 132–35. http://dx.doi.org/10.1299/jsmea.48.132.

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29

Jia, Hai Peng, Chun Dong Jiang, Bo Liu, Dong Xing Cao, and Chun Bo Jiang. "Nodal Evolutionary Computation Enhanced Level Set Algorithm for Structural Topology Optimization." Applied Mechanics and Materials 29-32 (August 2010): 337–42. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.337.

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This paper proposes an improved computational algorithm for structure topology optimization. It integrates the merits of Evolutionary Structure Optimization and Level Set Method (LSM) for structure topology optimization. Traditional LSM algorithm has some drawbacks, for instance, its optimal topology configuration is largely dependent on the structural topology initialization. Additionally, new holes cannot be evolved within the updated topology during the optimization iteration. The method proposed in this paper combines the merits of ESO techniques with the LSM scheme, allowing new holes to be automatically inserted in regions with low deformation energy at prescribed iterations of the optimization. The nodal neighboring region is a good selection. For complex structures in which holes cannot be properly inserted in advance, the proposed method considerably improves the ability of LSM to search the optimal topology. In addition to achieving more accurate results, the proposed method also yields higher efficiency during optimization. Benchmark problems are presented to show the effectiveness and robustness of the new proposed algorithm.
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30

Shi, Dong Yan, Jia Shan Han, Ling Cheng Kong, and Lin Lin. "Research on Evolutionary Topology Optimization in ANSYS." Key Engineering Materials 572 (September 2013): 547–50. http://dx.doi.org/10.4028/www.scientific.net/kem.572.547.

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Topology optimization function in ANSYS software is inefficient with the limitation of element types. By using the secondary developing language APDL and UIDL, the secondary development of bi-directional evolutionary structural optimization (BESO) method with volume constraint and stiffness maximization is completed in ANSYS. To suppress the checkerboard patterns, the elemental sensitivity numbers are recalculated by a filter method. To ensure the convergence of the optimization method in ANSYS, the elemental sensitivity numbers are updated by adding in their historical information. Two classic numerical examples are calculated to obtain the best topology structure. The numerical results indicate that the secondary method can solve the 2D and 3D problems effectively, which makes up for the deficiency of topology optimization part in ANSYS and broadens the application scope of the evolutionary optimization method.
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Teng, Xiao Yan, Jia Wei Tao, and Jia Shan Han. "Structural Stiffness Optimization Under Dynamic Loads." Applied Mechanics and Materials 668-669 (October 2014): 264–67. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.264.

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Most studies are focused on topology optimization techniques under external static loads. However, all forces are dynamic in real world. Mathematical optimization with dynamic loads is almost impossible in a large-scale problem. Thus, the bi-directional evolutionary structural optimization method (BESO) is extended to the topology optimization problem of structure under transient dynamic loading. Structural stiffness optimization is performed with the objective of reducing the mean compliance during the whole load-time history. By the dynamical analysis, the stiffness optimization model is established based on BESO method. In this method, the material volume is taken as the constraint condition, and the minimum mean compliance of structure is taken as the objective function. A cantilever under harmonic load has been taken as a numerical example to show the effectiveness of the proposed approach.
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32

Cui, Nannan, Shiping Huang, and Xiaoyan Ding. "An Improved Strategy for Genetic Evolutionary Structural Optimization." Advances in Civil Engineering 2020 (November 26, 2020): 1–9. http://dx.doi.org/10.1155/2020/5924198.

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Genetic evolutionary structural optimization (GESO) method is an integration of the genetic algorithm (GA) and evolutionary structural optimization (ESO). It has proven to be more powerful in searching for global optimal response and requires less computational efforts than ESO or GA. However, GESO breaks down in the Zhou-Rozvany problem. Furthermore, GESO occasionally misses the optimum layout of a structure in the evolution for its characteristic of probabilistic deletion. This paper proposes an improved strategy that has been realized by MATLAB programming. A penalty gene is introduced into the GESO strategy and the performance index (PI) is monitored during the optimization process. Once the PI is less than the preset value which means that the calculation error of some element’s sensitivity is too big or some important elements are mistakenly removed, the penalty gene becomes active to recover those elements and reduce their selection probability in the next iterations. It should be noted that this improvement strategy is different from “freezing,” and the recovered elements could still be removed, if necessary. The improved GESO performs well in the Zhou-Rozvany problem. In other numerical examples, the results indicate that the improved GESO has inherited the computational efficiency of GESO and more importantly increased the optimizing capacity and stability.
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33

Yang, Rui, Yang Liu, and Liang Zhou. "A Topology Optimization Method in Fuselage Flutter Model Design." Advanced Materials Research 199-200 (February 2011): 1297–302. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.1297.

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Airplane flutter scale model should maintain the load transfer characteristics of the original structure. It is a structural inverse problem for proper natural frequencies as well as structural simplification. This inverse problem could be solved by topology optimization. So based on bi-direction evolutionary structural optimization (BESO) method, a topology method for designing fuselage flutter model is presented. Facing porous and irregular shape often appears in topology optimization, a regular shaped grid frame structure consisted of the finite elements is discussed, including its internal mapping relationship and boundary conditions. The ratio criterion for structural modification is raised in this structural topology optimization using frequency sensitivity. Finally, this topology optimization method is applied to cylindrical fuselage flutter model design, result shown that the proposed approach is feasible to achieve given natural frequencies, maintains the character of inner frame structure completely, and the similarity between optimized structure and original structure is achieved.
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34

Zhang, Wen Hui, Yao Ting Zhang, and Guo Qing Li. "Evolutionary Structural Topology Optimization for Cantilever Construction of Continuous Rigid-Frame Bridge." Applied Mechanics and Materials 90-93 (September 2011): 18–22. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.18.

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In this paper, an evolutionary structural optimization (ESO) method based on the Ishai stress and artificial material elements was used to optimize the structure topology of the cantilever construction of an actual continuous rigid-frame bridge with a new fasting structual system. In iteration process, the engineering infeasible solution was dealt with the engineering constraints; the checkboard problem was solved with the filter scheme, which used the average Ishai stress of the elements located inside the filter domain instead of that located at the center; moreover, the local stress concentration problem was also solved with the scheme of gradully decreasing the filter radius. Thus the iteration ran smoothly, then the optimizing results were obtained finally. The final topology model illustrates the feasibility of the fasting structual system, and the proposed methods are effective to the optimization design for the actual bridge.
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35

Richard, Marc J., Mohamed Bouazara, Laouhari Khadir, and Guoqiang Q. Cai. "STRUCTURAL OPTIMIZATION ALGORITHM FOR VEHICLE SUSPENSIONS." Transactions of the Canadian Society for Mechanical Engineering 35, no. 1 (2011): 1–17. http://dx.doi.org/10.1139/tcsme-2011-0001.

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Stringent tolerances on mechanical components have created increasingly severe demands on the quality of new mechanical designs. The mathematical models used to analyze the various types of mechanical systems these days need to incorporate an optimization algorithm capable of minimizing the levels of vibrations coming from varied sources. The suggested method is based on the parallel combination of three methods; the Rayleigh-Ritz approach (to determine the first eigenfrequencies) which is incorporated into an efficient multicriterion optimization process based on the ESO (Evolutionary Structural Optimization) method and the finite element software ABAQUS. The analytical resolution and the numerical calculations of the mechanical component are, finally, validated by an experimental set-up which exploits a frequency analyser, acceleration sensors and an excitation hammer. The effectiveness of this approach is also demonstrated in the analysis of an upper car suspension arm. By gradually removing material from the initial car suspension design, the frequency of the component can be controlled in order to optimize the structural constraints.
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36

Shobeiri, Vahid. "Topology optimization using bi-directional evolutionary structural optimization based on the element-free Galerkin method." Engineering Optimization 48, no. 3 (2015): 380–96. http://dx.doi.org/10.1080/0305215x.2015.1012076.

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37

Hu, Xing Guo, and He Ming Cheng. "Evolutionary Structural Optimization Using the Minor Value of Material Efficiency." Advanced Materials Research 945-949 (June 2014): 1223–26. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1223.

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In the tradition Evolutionary Structural Optimization (ESO), the maximum value of inefficient material efficiency equates to the product of the rejection rate and the maximum value of all material efficiency. However, the rejection rate cannot be adjusted flexibly according to the trend of optimization, and the maximum value of all material efficiency may mutate abruptly (become larger or smaller). These two factors may cause that material may sometimes be removed less, sometimes too much. In view of the defect of the traditional evolutionary structural optimization, the Evolutionary Structural Optimization Using the Minor Value of Material Efficiency (ESO-MVME) is proposed in this paper. The maximum value of the inefficient material is close to the minimum value of material efficiency, and has nothing to do with the reject rate and the maximum value of material efficiency. The study finds that the ESO-MVME method has a better applicability than the traditional ESO, and can obtain a better optimization result.
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38

Nha, Chu D., Y. M. Xie, and G. P. Steven. "An evolutionary structural optimization method for sizing problems with discrete design variables." Computers & Structures 68, no. 4 (1998): 419–31. http://dx.doi.org/10.1016/s0045-7949(98)00062-5.

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39

Li, Yan, Xiao Dong Huang, Yi Min Xie, and Shi Wei Zhou. "Bi-Directional Evolutionary Structural Optimization for Design of Compliant Mechanisms." Key Engineering Materials 535-536 (January 2013): 373–76. http://dx.doi.org/10.4028/www.scientific.net/kem.535-536.373.

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This research presents a topology optimization approach based on Bi-directional Evolutionary Structural Optimization (BESO) for optimal design of compliant mechanisms. Due to the complexity of the design for various compliant mechanisms, a new multi-objective optimization model is established by considering the mechanism flexibility and structural stiffness simultaneously. The sensitivity analysis is performed by applying the adjoint sensitivity approach to both the kinematical function and the structural function. The sensitivity numbers are derived according to the variation of the objective function with respect to the design variables. Some numerical examples are given to demonstrate the effectiveness of the proposed method for the design of various compliant mechanisms.
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40

Nha, Chu Duc. "A method for selecting optimal sections for members of skeletal structures." Vietnam Journal of Mechanics 23, no. 4 (2001): 234–46. http://dx.doi.org/10.15625/0866-7136/9955.

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This paper presents a simple method for selecting optimal sections for members of skeletal structures from an initially given set of sections. This is an extension of evolutionary structural optimization (ESO) to the sizing optimization problem with discrete design variables. Member sensitivity index for section sizing is derived from the optimality criterion. Optimization process is an iterative process of analysis, sensitivity calculation and section selection until optimality criterion is satisfied or constraints are violated. The proposed optimization procedure has been implemented into a structural analysis and optimization program called FEMOPT written on MATLAB programming language. Illustrative examples demonstrate the effectiveness of the proposed method.
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41

Kozlov, Alexey V., and Yuri P. Kondratenko. "SEARCH FOR OPTIMAL FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS." Journal of Automation and Information sciences 1 (January 1, 2021): 55–75. http://dx.doi.org/10.34229/0572-2691-2021-1-5.

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Contemporary research in the field of creation and development of intelligent systems based on fuzzy logic is carried out mainly in the direction of developing highly efficient methods for their synthesis and structural-parametric optimization. In recent years, due to the intensive development of information technologies and computer hardware, bioinspired intelligent techniques of global search are quite promising for solving problems of synthesis and optimization of fuzzy systems, which include evolutionary and swarm methods, that simulate the processes of natural selection, as well as collective behavior of various groups of social animals, insects and microorganisms in nature. This paper is devoted to the development and study of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The obtained method allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multicriteria structural optimization of various fuzzy systems in order to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. In the proposed method for finding the global optimum of the problem being solved, the iterative procedures are carried out on the basis of combination of several different bioinspired evolutionary algorithms with subsequent analysis of the results obtained and the choice of the best variant of the membership function vector. The paper outlines the theoretical foundations and information model for the implementation of the computational step-by-step method for structural optimization of fuzzy systems, as well as presents various options for carrying out its search procedures. In particular, the features of the application and adaptation to the search problem to be solved of such bioinspired evolutionary algorithms as genetic, artificial immune systems and biogeographic are discussed.
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42

Abdi, M., I. Ashcroft, and R. D. Wildman. "Topology Optimization of Geometrically Non-Linear Structures Using Iso-XFEM Method." Key Engineering Materials 627 (September 2014): 121–24. http://dx.doi.org/10.4028/www.scientific.net/kem.627.121.

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Iso-XFEM is an evolutionary-based topology optimization method which couples the extended finite element method (X-FEM) with an isoline/isosurface optimization approach, enabling a smooth and accurate representation of the design boundary in a fixed-grid finite element mesh. This paper investigates the application of the Iso-XFEM method to the topology optimization of structures which experience large deformation. The total Lagrangian formulation of the finite element method is employed to model the geometrically non-linear behaviour and equilibrium is found by implementing the Newton-Raphson method in each evolution. A cantilever beam is considered as a test case and the Iso-XFEM solutions obtained from linear and non-linear designs are compared with bi-directional evolutionary structural optimization (BESO) solutions.
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43

Zhang, Wenhui, Jing Liu, Zhenhua Xia, Yaoting Zhang, and Niuben Yu. "Efficient Local Level Set Algorithm Combined with Bidirectional Evolutionary Criterion Using Discrete Variables and its Appliance to Topology and Shape Optimization." Journal of Physics: Conference Series 2185, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1742-6596/2185/1/012040.

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Abstract Many level set methods (LSMs) have been used for topology and shape optimization. An efficient topology optimization algorithm is proposed by combining a new bi-directional evolutionary criterion (BEC) with the local LSM (LLSM). The BEC is established using multiple discrete variables (DVs) and introduced into the bi-directional evolutionary structural optimization (BESO) method so that the DV-BESO method obtained can be applied to topology optimization. The LLSM is used instead of the global LSM due to its high efficiency. In the LLSM, the computational efficiency can be further improved by solving a distance regularized equation without the re-initialization procedure. Two benchmarks show the high efficiency of this proposed combined algorithm for topology and shape optimization.
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44

Xue, Hongjun, Haiyang Yu, Xiaoyan Zhang, and Qi Quan. "A Novel Method for Structural Lightweight Design with Topology Optimization." Energies 14, no. 14 (2021): 4367. http://dx.doi.org/10.3390/en14144367.

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Topological optimization is an innovative method to realize the lightweight design. This paper proposes a hybrid topology optimization method that combines the SIMP (solid isotropic material with penalization) method and genetic algorithm (GA), called the SIMP-GA method. In the method, SIMP is used to update the chromosomes, which can accelerate convergence. The filtering scheme in the SIMP method can filter unconnected elements to ensure the connectivity of the structure. We studied the influence of varying the filtering radius on the optimized structure. Simultaneously, in the SIMP-GA method, each element is regarded as a gene, which controls the population number to a certain extent, reduces the amount of calculation, and improves the calculation efficiency. The calculation of some typical examples proves that the SIMP-GA method can obtain a better solution than the gradient-based method. Compared with the conventional genetic algorithm and GA-BESO (Bi-directional Evolutionary Structural Optimization) method, the calculation efficiency of the proposed method is higher and similar results are obtained. The innovative topology optimization method could be an effective way for structural lightweight design.
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45

Kulkarni, Dr Jyoti S. "Genetic Algorithm Approach for Image Fusion: A Simple Method and Block Method." International Journal of Innovative Technology and Exploring Engineering 11, no. 6 (2022): 16–21. http://dx.doi.org/10.35940/ijitee.f9895.0511622.

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The sensors available nowadays are not generating images of all objects in a scene with the same clarity at various distances. The progress in sensor technology improved the quality of images over recent years. However, the target data generated by a single image is limited. For merging information from multiple input images, image fusion is used. The basis of image fusion is on the image acquisition as well as on the level of processing and under this many image fusion techniques are available. Several input image acquisition techniques are available such as multisensor, multifocus, and multitemporal. Also, image fusion is performed in four different stages. These levels are the level of the signal, pixel level, level of feature, and level of decision-making. Further, the fusion methods are divided into two domains i.e spatial and frequency domains. The fusion in spatial domain images uses inputs directly to work on pixels, while the transition refers to frequency domain image fusion on input images before fusion. The limitation of spatial domain image fusion is spectral degradation. To overcome this limitation, the fusion of transform domain images is preferred which uses several transforms. The results generated by transform methods are superior to spatial domain methods. But there is a scope to improve the results or to find the optimized results. Optimization can be achieved by using evolutionary approaches. The evolutionary computation approach is an effective way of finding the required solution for a complex problem. An evolutionary algorithm is a guided random search used for optimization. The biological model of evolution and natural selection inspires it. The different types of evolutionary computing algorithms include Genetic algorithm, Genetic Programming, Evolutionary programming, Learning Classifier System, Ant Colony Optimization, Artificial Bee Colony Optimization, Particle Swarm Optimization, Evolution strategy, Swarm intelligence, Tabu Search, Cuckoo Search, etc. Three genetic algorithm-based image fusion techniques are proposed: a genetic algorithm with one population, a genetic algorithm with separate populations, and a block method. In the block method, an array of numbers in one chromosome is generated. The result obtained by the proposed techniques are compared with existing methods and observed that the results are improved. The graphical representation of performance parameters reflects that the block method is better.
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46

Li, Kai, Yanyun Yu, Jingyi He, Decai Zhao, and Yan Lin. "Structural Optimization of Hatch Cover Based on Bi-directional Evolutionary Structure Optimization and Surrogate Model Method." Journal of Shanghai Jiaotong University (Science) 23, no. 4 (2018): 538–49. http://dx.doi.org/10.1007/s12204-018-1975-0.

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47

Sekulski, Zbigniew. "Ship Hull Structural Multiobjective Optimization by Evolutionary Algorithm." Journal of Ship Research 58, no. 02 (2014): 45–69. http://dx.doi.org/10.5957/jsr.2014.58.2.45.

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An evolutionary algorithm for multiobjective optimization of the structural elements of the large spatial sections of ships is presented. The evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area on a high-speed vehicle-passenger catamaran structure with several design variables such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinal and transversal structural members. Details of the computational models were at the level typical for conceptual design. Scantlings were analyzed using the selected rules of a classification society. The results of numerical experiments with the use of the developed algorithm are presented. They show that the proposed genetic algorithm can be a foundation of the effective multiobjective optimization tool for ship structure optimization. Further development of the tool should include more advanced methods for ship structural analysis.
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48

Li, Yu, and Yi Min Xie. "Evolutionary Topology Optimization of Spatial Steel-Concrete Structures." Journal of the International Association for Shell and Spatial Structures 62, no. 2 (2021): 102–12. http://dx.doi.org/10.20898/j.iass.2021.015.

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Topology optimization techniques based on finite element analysis have been widely used in many fields, but most of the research and applications are based on single-material structures. Extended from the bi-directional evolutionary structural optimization (BESO) method, a new topology optimization technique for 3D structures made of multiple materials is presented in this paper. According to the sum of each element's principal stresses in the design domain, a material more suitable for this element would be assigned. Numerical examples of a steel- concrete cantilever, two different bridges and four floor systems are provided to demonstrate the effectiveness and practical value of the proposed method for the conceptual design of composite structures made of steel and concrete.
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49

Fernandes, Francisco Jose, and Renato Pavanello. "Topology optimization of adhesive material in a single lap joint using an evolutionary structural optimization method and a cohesive zone model as failure criterion." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 236, no. 4 (2021): 757–78. http://dx.doi.org/10.1177/14644207211056945.

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This paper presents a study of the use of a structural optimization process coupled to a failure model in the adhesive material in single lap bonded joints. The critical point of these joints is in the region of the adhesive material, which performs the function of stress transmission between the structural elements. Owing to the single lap bonded joints shape, the applied loads are eccentric about the joint axis, resulting in a concentration of stress on the overlapping ends. In this study, numerical simulations in two and three dimensions were performed through finite element analysis to model an single lap bonded joints. An optimization procedure based on the bidirectional evolutionary structural optimization method was used to minimize the single lap bonded joints compliance under volume constraints. The design domain considered was restricted to the adhesive region. The cohesive zone model and the bidirectional evolutionary structural optimization method were simultaneously applied. The numerical results for two types of adhesives, with ductile and brittle failure behavior, enabled the establishment of mechanisms for determining the efficient positioning and quantity of adhesive materials.
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50

Leu, L. J., C. W. Huang, and J. J. Chou. "Topology Optimization of Elastic-Plastic Structures." Journal of Mechanics 19, no. 4 (2003): 431–42. http://dx.doi.org/10.1017/s1727719100003282.

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ABSTRACTThe evolutionary structural optimization method is improved and extended to elastic-plastic topology optimization for the first time. An adaptive rejection ratio is proposed to control the number of removal elements without destroying the symmetric pattern in each evolution. Two performance indices suitable for elastic-plastic topology optimization are also proposed and examined. The performance indices can be used to investigate the material efficiency of structures in different evolutionary stages, and to serve as stop criteria in the evolutionary process. Moreover, an interactive special purpose computer analysis and graphics system is developed to visualize the topology in the evolutionary process. Finally, the effects of yield stress, Young's modulus, and the prescribed displacement in an elastic-plastic analysis on the obtained topology are discussed.
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