Graph representation for structural topology optimization using genetic algorithms

Wang, S.Y.; Tai, Kang

doi:10.1016/j.compstruc.2004.05.005

Cited by 69 publications

(34 citation statements)

References 27 publications

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“…As an important alternative approach within this family, the powerlaw approach [7], which is also called the solid isotropic microstructure with penalization (SIMP) method [8] and originally introduced by Bendsøe [9], has got a fairly general acceptance in recent years [3] because of its computational efficiency and conceptual simplicity. However, it does not directly attack the original 0-1 problem [10] and thus tends to converge to a local optimal topology with blurry boundary or undesirable checkerboard patterns [3,6,10,11] or converge to an infeasible solution to the original 0-1 problem [12]. Since the problem of non-existence of solutions (ill-posedness) is not resolved, priori restrictions on the admissible design configurations such as a perimeter constraint, a gradient constraint or with filtering techniques [7,10] must be introduced [13].…”

Section: Introductionmentioning

confidence: 98%

“…Since the seminal work of Holland [24] and the comprehensive study of Goldberg [25], GAs have become an increasingly popular optimization tool for many areas of research. More recently, GAs have been gradually recognized as a powerful and robust stochastic global search method for structural topology optimization [6,11,[26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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An enhanced genetic algorithm for structural topology optimization

Wang

Tai

Wang

2005

Int. J. Numer. Meth. Engng

Self Cite

121

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SUMMARYGenetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problemspecific knowledge. The original discrete black-and-white (0 -1) problem is directly solved by using a bit-array representation method. To address the related pronounced connectivity issue effectively, the four-neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well-posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge-free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM-based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near-optimum solutions and viable topologies with checkerboard free, mesh independent and hinge-free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem-specific knowledge can be a powerful global search tool for structural topology optimization.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

An enhanced genetic algorithm for structural topology optimization

Wang

Tai

Wang

2005

Int. J. Numer. Meth. Engng

Self Cite

121

View full text Add to dashboard Cite

show abstract

“…The use of graph theory for GAs is one of the new developments in structural optimization problem. Wang and Tai [41] devised a graph representation for the topology-related design variables in structural optimization problems. Kaveh and Kalatjari [42] utilized the graph theory for representation of the size-related design variables and force method for structural analysis.…”

Section: Evolutionary-based Optimization Studiesmentioning

confidence: 99%

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Talaslioglu

2009

Modelling and Simulation in Engineering

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A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

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“…Genetic algorithms (GAs) are a class of stochastic relaxation techniques that are applicable to the solution of a wide variety of optimization engineering problems [1][2][3][4][5][6][7][8][9][10] by emanating the evolutionary behaviour of biological systems. They are global optimizers due to their population search.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Parameters Effect on the Optimal Structural Design Search

Maskaoui¹

2017

IOSR JMCE

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Graph representation for structural topology optimization using genetic algorithms

Cited by 69 publications

References 27 publications

An enhanced genetic algorithm for structural topology optimization

An enhanced genetic algorithm for structural topology optimization

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Genetic Algorithm Parameters Effect on the Optimal Structural Design Search

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