Generation and Classification of Structural Topologies With Genetic Algorithm Speciation

Duda, James W.; Jakiela, Mark J.

doi:10.1115/1.2828774

Cited by 43 publications

(18 citation statements)

References 3 publications

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“…Whereas binary bit array representation do not have the geometric bias drawback as in the case of binary bit strings. This is because the adjacency relation between the design cells in the binary bit array is same as that of the design domain [18,21]. All the topologies are represented by binary bit arrays.…”

Section: Role Of Genetic Algorithms In Topology Optimizationmentioning

confidence: 99%

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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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Section: Role Of Genetic Algorithms In Topology Optimizationmentioning

confidence: 99%

“…Block crossover [18,21] is used. For any two parents that are randomly selected, the binary bit arrays are subdivided into 9 blocks with two horizontal and vertical lines respectively.…”

Section: Block Crossover and Bit-flip Mutationmentioning

confidence: 99%

Corner Elimination in Discrete Topology Optimization of Compliant Mechanisms

Zhou

Jangam

2013

Volume 4A: Dynamics, Vibration and Control

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“…Onepoint or multi-point crossovers have the ability to exchange horizontal bands of the parents thereby resulting in geometric biasing. The adjacency relationship of design cells in binary bit array representation is same as that of design domain that results to overcome this geometric biasing [11][12][13]. Alongside bit array representation morphological representation was also introduce for GA based discrete topology optimization [14][15][16].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…In this research Block crossover strategy is employed [11][12][13]. Block crossovers are formed when the binary bit -arrays of two parents are cut into nine blocks by two horizontal and vertical lines.…”

Section: Block Crossoversmentioning

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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“…Extending the idea of using GA, the structural optimization problems with different kinds of objective functions and constraints were solved. Later, a two-dimensional crossover operator which divides the design domain into four rectangular sub-domains was used for further improving the GA evolved designs [3,10,13]. After the feasibility of GA-based optimal designs, the study [14] emphasized on its flexibility and demonstrated its potentialities over the classical optimization methods.…”

Section: Introductionmentioning

confidence: 99%

A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms

Sharma

Deb

Kishore

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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A domain-specific crossover with NSGA-II algorithm is proposed for generating the compliant mechanisms. The newcrossover divides a design-domain into sub-domains and exchanges the areas of sub-domains between two-parents of GA population. A single objective analysis of minimizing the weight of structure is performed first and topology is named as reference-design. Thereafter, a bi-objective optimization problem is formulated with primary and helper objectives of minimizing the weight and maximizing the diversity of structures with respect to reference-design. The diversity is evaluated by comparing the dissimilarities in bit value at each gene position of binary strings of referencedesign and GA evolved structure. Motivation behind the use helper-objective and new-crossover is to generate a diverse set of non-dominated NSGA-II solutions which accomplish a task of tracing a user-defined path. Therefore, both optimization problems are subjected to constraints of allowed deviation at precision points of a prescribed path with a constraint on stress, and are solved using a local search based NSGA-II procedure. During the optimization, compliant mechanisms can have any support and loading positions with varying input-displacement magnitude within their respective domains. Obtained results are further compared with the results of another-crossover operator based study which was used to generate compliant mechanisms.

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Generation and Classification of Structural Topologies With Genetic Algorithm Speciation

Cited by 43 publications

References 3 publications

Corner Elimination in Discrete Topology Optimization of Compliant Mechanisms

Corner Elimination in Discrete Topology Optimization of Compliant Mechanisms

Discrete Topology Optimization of Structures Without Uncertainty

A domain-specific crossover and a helper objective for generating minimum weight compliant mechanisms

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