Structural topology design optimization using Genetic Algorithms with a bit-array representation

Wang, S.Y.; Tai, Kang

doi:10.1016/j.cma.2004.09.003

Cited by 133 publications

(136 citation statements)

References 33 publications

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“…198.064 (6) 252.258 (7) 285.161 (8) 363.225 (9) 388.386 (10) 494.193 (11) 506.451 (12) 641.289 (13) 645.160 (14) 729.031 (15) 792.256 (16) 816.773 (17) 939.998 (18) 1008.385 (19) 1045.159 (20) 1161.288 (21) 1283.868 (22) 1374.191 (23) 1535.481 (24) 1690.319 (25) 1696.771 (26) 1858.061 (27) 1890.319 (28) 1993.544 (29) 2180.641 (30) 2238.705 (31) 2290.318 (32) 2341.931 (33) 2477.414 (34) 2496.769 (35) 2503 …”

Section: The 72-bar Spatial Truss Exampleunclassified

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

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Section: The 72-bar Spatial Truss Exampleunclassified

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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“…Hence, the original '0-1' optimization problem was attacked directly by using a bit-array representation and a genetic algorithm. The work of Sandgren and his co-workers, using bit-array representation, has been extended by Jakiela and his coworkers [3][4][5], by Schoenauer and his co-workers [6,7,9], by Fanjoy and Crossley [8,10], and, more recently, by Wang and Tai [11]. Although all these extensions can well prevent checkerboard patterns by exploiting a connectiva Corresponding author: matthieu.domaszewski@utbm.fr ity restriction, the other numerical instabilities in structural topology optimization such as mesh dependency and one-node connections still exist.…”

Section: Introductionmentioning

confidence: 99%

An adjacency representation for structural topology optimization using genetic algorithm

Sid

Domaszewski

Peyraut

2007

Int. J. Simul. Multidisci. Des. Optim.

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-A new approach for continuum structural topology optimization using genetic algorithms is presented in this paper. The proposed approach is based on a representation by adjacency where the principle is founded on the concept of connectivity of finite elements, considered as cells. This principle is expressed by an adjacency matrix similar to that used in the graph theory. The encoding of the structure solutions uses this matrix by transforming it into a binary string. The research of optimal solution, i.e. the optimal material distribution, is interpreted in this approach by the determination of the connectivity of elements (cells). Using density variable, the approach has some common points with the homogenization techniques. The proposed approach is tested with simple benchmark applications.

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“…In the bit string representation, the adjacent bits may not correspond to the neighboring vertical design cells in a discretized design domain and are far away from each other which results in geometric bias because one-point or multi-point crossovers can only exchange the horizontal bands of the parents. This drawback is restricted in the binary bit-array representations as the binary bit array representations do not have the geometric bias drawback since tile adjacency relationship of design cells in binary bit array representation is exactly the same as that in the design domain [12][13][14]. The GA based discrete topology optimization also uses the morphological representation apart from the bit-array representation for discrete topology optimization [15][16][17].…”

Section: Chapter IV Optimization Algorithmmentioning

confidence: 99%

The Uncertainty Elimination in Discrete Topology Optimization of Compliant Mechanisms

Zhou

Kandala

2013

Volume 6A: 37th Mechanisms and Robotics Conference

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The uncertainty in topology optimization leads to ambiguity caused by different topology solutions. This uncertainty is caused by either having point connections or grey cells which are eliminated by using hybrid discretization model for discrete topology optimization. Mesh dependence also contributes to topology uncertainty. When the design domain of a topology is discretized differently, its solution depends on the type of discretization which leads to uncertainty caused by mesh dependence. To address the problem of mesh dependence, the genus based topology optimization strategy is introduced in this thesis. This approach deals with controlling of the genus of an optimized compliant mechanism in the process of optimizing the topology. There is no uncertainty caused by mesh dependence when this strategy is implemented with hybrid discretization for topology optimization. An example is considered to demonstrate the introduced approach.iv ACKNOWLEDGEMENT

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Structural topology design optimization using Genetic Algorithms with a bit-array representation

Cited by 133 publications

References 33 publications

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

An adjacency representation for structural topology optimization using genetic algorithm

The Uncertainty Elimination in Discrete Topology Optimization of Compliant Mechanisms

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