Genetic Algorithm-Based Structural Topology Design With Compliance and Topology Simplification Considerations

Chapman, Colin D.; Jakiela, Mark J.

doi:10.1115/1.2826862

Cited by 92 publications

(37 citation statements)

References 15 publications

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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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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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“…Genetic algorithms are popular for their vast usage in the topology optimization of structures [5][6]. Binary bit strings are traditionally used to represent topologies in genetic algorithms.…”

Section: Role Of Genetic Algorithms In Topology Optimizationmentioning

confidence: 99%

“…Hence topology optimization is basically a discrete optimization problem. Stochastic optimization techniques like Genetic Algorithms (GA's) [5][6] can be used for discrete topology optimization. Binary design variables are used to represent either material presence or absence.…”

Section: Topology Optimization Of Distributed Compliant Mechanismsmentioning

confidence: 99%

Corner Elimination in Discrete Topology Optimization of Compliant Mechanisms

Zhou

Jangam

2013

Volume 4A: Dynamics, Vibration and Control

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State of material can be either void or solid in the optimization of discrete topologies. In the present discrete topology optimization, one main issue is the unsmooth boundaries. These are created due to subdivision techniques and exist as sharp corners in the topology solution. Even though outer corner cutting and inner corner filling method can reduce the corners, it cannot completely eliminate them. Corners that are at 90° are reduced to 135° using the corner handling

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“…Therefore, in order to optimize the topology of a continuum structure, its design domain is discretized into design cells so as to decide its binary material state that is either solid or void. For optimizing the continuum structure discretely, stochastic algorithms such as genetic algorithms are utilized [2][3].…”

Section: Discrete Topology Optimization Of Continuum Structuresmentioning

confidence: 99%

Discrete Topology Optimization of Structures Without Uncertainty

Zhou

Kolavennu

2013

Volume 4A: Dynamics, Vibration and Control

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The objective of topology optimization of a structure is to design its layout optimally.The topology of a structure is defined by its genus or number of handles. When topology of a structure is optimized, its topology might change whenever the material state of the design cell is switched from solid to void or vice versa. This leads to uncertainty fostering ambiguous topology solutions. In order to avoid this uncertainty, discrete topology optimization technique is utilized.In discrete topology optimization procedure all the design cells are considered to be either solid or void, thereby grey cell or intermediate material cells are eliminated and the uncertainty is avoided.Existence of point connections might cause topology uncertainty. This uncertainty can be eradicated through design domain discretization. In optimization process, hexagonal discretization, hybrid discretization, and modified quadrilateral discretization techniques are primarily used to discretize the design domain. In these discretization approaches, any two neighboring cells share an edge and so there will never be point connections. The hybrid discretization is primarily utilized as it has more number of local topology search directions, in comparison to other discretization approaches.iv However, the topology optimization solutions pertaining to structures are uncertain, if the design domain is discretized differently. This uncertainty is called as mesh dependence problem.In order to eliminate it, the degree of genus (DOG) based topology optimization strategy is introduced. In this strategy, the genus of an optimized structure is constrained during topology optimization process. There exists no topology uncertainty problem even if the design domain is discretized with different discretization approaches. This introduced strategy is used for discrete topology optimization of structures that have multiple loading points. The accuracy of this strategy is demonstrated with some examples.v ACKNOWLEDGEMENT

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Genetic Algorithm-Based Structural Topology Design With Compliance and Topology Simplification Considerations

Cited by 92 publications

References 15 publications

An adjacency representation for structural topology optimization using genetic algorithm

An adjacency representation for structural topology optimization using genetic algorithm

Corner Elimination in Discrete Topology Optimization of Compliant Mechanisms

Discrete Topology Optimization of Structures Without Uncertainty

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