HPC Implementation of the Multipoint Approximation Method for Large Scale Design Optimization Problems Under Uncertainty

2018 ◽  
pp. 296-306
Author(s):  
Vassili Toropov ◽  
Yury Korolev ◽  
Konstantin Barkalov ◽  
Evgeny Kozinov ◽  
Victor Gergel
Keyword(s):  
Design Optimization ◽  
Approximation Method ◽  
Large Scale ◽  
Optimization Problems ◽  
Multipoint Approximation ◽  
Scale Design

scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

scholarly journals An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pooja Verma ◽  
Raghav Prasad Parouha
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Structural Engineering ◽  
Large Scale ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Solution Space ◽  
Selection Strategy ◽  
Engineering Design Optimization ◽  
Global And Local

AbstractAn advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

scholarly journals Deep Learning-Based Accuracy Upgrade of Reduced Order Models in Topology Optimization

2021 ◽  
Vol 11 (24) ◽  
pp. 12005
Author(s):  
Nikos Ath. Kallioras ◽  
Alexandros N. Nordas ◽  
Nikos D. Lagaros
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Computational Effort ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Learning Techniques ◽  
Complex Models ◽  
Scale Design

Topology optimization problems pose substantial requirements in computing resources, which become prohibitive in cases of large-scale design domains discretized with fine finite element meshes. A Deep Learning-assisted Topology OPtimization (DLTOP) methodology was previously developed by the authors, which employs deep learning techniques to predict the optimized system configuration, thus substantially reducing the required computational effort of the optimization algorithm and overcoming potential bottlenecks. Building upon DLTOP, this study presents a novel Deep Learning-based Model Upgrading (DLMU) scheme. The scheme utilizes reduced order (surrogate) modeling techniques, which downscale complex models while preserving their original behavioral characteristics, thereby reducing the computational demand with limited impact on accuracy. The novelty of DLMU lies in the employment of deep learning for extrapolating the results of optimized reduced order models to an optimized fully refined model of the design domain, thus achieving a remarkable reduction of the computational demand in comparison with DLTOP and other existing techniques. The effectiveness, accuracy and versatility of the novel DLMU scheme are demonstrated via its application to a series of benchmark topology optimization problems from the literature.

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