DETERMINING PRECISION OF PROJECTING THE ENGINEERING STRUCTURE DESIGN ON THE LOCATION

2019 ◽  
Vol 0 (84) ◽  
pp. 43
Author(s):  
N. O. Arsenieva ◽  
O. S. Synovets
Keyword(s):  
Structure Design ◽  
Engineering Structure

An Optimization Method for Optimal Engineering Structure Design Combining Intelligent Optimization Algorithm and CAE Code

2011 ◽  
Vol 399-401 ◽  
pp. 2296-2300
Author(s):  
Wen Jie Peng ◽  
Rui Ge ◽  
Ming Kai Gu
Keyword(s):  
Analytical Solution ◽  
Normal Stress ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Structure Design ◽  
Stacking Sequence ◽  
Intelligent Optimization ◽  
Engineering Structure ◽  
Intelligent Optimization Algorithm ◽  
Computer Aided

This paper presents an optimization method for optimal engineering structure design. An interface procedure is essentially developed to combine the intelligent optimization algorithm and computer aided engineering (CAE) code. An optimization example is carried out to minimize the interlaminar normal stress of a laminate which affect the delamination failure of a laminate via arranging the stacking sequence. The analytical solution is calculated to validate the accuracy of optimization results.

scholarly journals A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Qiuyu Li ◽  
Zhiteng Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Structure Design ◽  
Local Optimum ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Engineering Structure ◽  
Inertia Weight

Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

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