Structural topology optimisation using simulated annealing with multiresolution design variables

Bureerat, Sujin; Limtragool, Jumlong

doi:10.1016/j.finel.2008.04.002

Cited by 54 publications

(24 citation statements)

References 19 publications

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“…Furthermore, the ground element filtering technique is also used in this study. The ground element filtering technique (GEF technique) is a simple numerical scheme that can apply to all kinds of optimizers, which can prevent the checkerboard pattern problem and at the same time decrease the number of design variables [8,18,19]. The idea uses two mesh grids of design domain with different resolutions.…”

Section: Topological Designs With Ground Element Filteringmentioning

confidence: 99%

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Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Sleesongsom

Bureerat

2018

Applied Sciences

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This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.

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Section: Topological Designs With Ground Element Filteringmentioning

confidence: 99%

“…Therefore, this technique has been proved to be an efficient technique to suppress the checkerboard problem. Next, the details of GEF technique are seen in [8,18,19]. Later, a method for solving checkerboard pattern was presented by Guirguis and Aly [20].…”

Section: Topological Designs With Ground Element Filteringmentioning

confidence: 99%

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

Sleesongsom

Bureerat

2018

Applied Sciences

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“…Binary simulated annealing (BSA) [50] used binary codes with exponentially decreasing temperature. The starting and ending temperature are set to be 10 and 0.001, respectively.…”

Section: Flight Dynamic Control Optimisation Problemmentioning

confidence: 99%

“…Apart from GA, other MHs using a binary string representing a design solution include a univariate marginal distribution algorithm (UMDA) [48], population-based incremental learning (PBIL) [24], binary particle swarm optimisation (BPSO) [49], binary simulated annealing (BSA) [50], binary artificial bee colony algorithm based on genetic operator (GBABC) [51], binary quantuminspired gravitational search algorithm (BQIGSA) [52], and self-adaptive binary variant of a differential evolution algorithm (SabDE) [53]. With the popularity of GA, a binarycode MH has been rarely developed and proposed while its real-code counterparts have over a hundred different search concepts reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.

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“…On the other hand, SA has also been used as a method for optimizing topology by altering an initial solution and accepting the new solution (i.e., neighbor) if the value of the objective function improves or even if it worsens with certain probability [16,17]. This methodology generally yields disconnected topologies, although this has been improved by the use of multiresolution design variables [18], but also at a high computational cost.…”

Section: Summary Of Existing Topology Optimization Approachesmentioning

confidence: 99%