2021
DOI: 10.1177/00368504211023277
|View full text |Cite
|
Sign up to set email alerts
|

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

Abstract: In the present work, the working state of the crane leg is analyzed and discussed, and its structure is optimized. SolidWorks software is used for modeling; ANSYS software is used for finite element analysis. First of all, the constrained finite element method (CFEM) is used to analyze the linear eigenvalue buckling and geometric nonlinear buckling of outriggers with different cross-section shapes. Prove that the curved leg has certain advantages in buckling. At the same time, analyzing the leg along a differe… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 46 publications
0
4
0
Order By: Relevance
“…Parallel evaluation can partly reduce optimization time [48], and culture speeds up the population's evolution more than chromosomes (each chromosome represents a solution in the population space) [49]. Accumulated experience that is potentially accessible to all individuals is called culture, which is used in problem-solving activities [50]. The knowledge extracted by identifying patterns in the population's problem-solving experiences influences the generation of new solutions [51].…”
Section: The Proposed Optimization Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Parallel evaluation can partly reduce optimization time [48], and culture speeds up the population's evolution more than chromosomes (each chromosome represents a solution in the population space) [49]. Accumulated experience that is potentially accessible to all individuals is called culture, which is used in problem-solving activities [50]. The knowledge extracted by identifying patterns in the population's problem-solving experiences influences the generation of new solutions [51].…”
Section: The Proposed Optimization Methodsmentioning
confidence: 99%
“…The GA and PSO are the optimization algorithms widely applied to HPO studies in deep learning [1,8]. GA is far more successful in complex networks such as CNNs, but eliminates previous information by changing the population every iteration [50]. PSO shares information between the particles and is popular on the smaller networks [29].…”
Section: The Proposed Optimization Methodsmentioning
confidence: 99%
“…As a global optimization method, multi-island genetic algorithm has better global solving ability and computational efficiency compared with traditional genetic algorithm [20][21][22]. Zu Lei et al [23] established the sectionalization-based reinforcement optimization model through multi-island genetic algorithm to optimize the reinforcing layers and angles according to the stress distribution on domes.…”
Section: Experimental Study and Finite Element Analysismentioning
confidence: 99%
“…Lin's work, the genetic algorithm with differential evolution (GA-DE) hybrid evolutionary algorithm and the real-valued genetic algorithm (RGA) with arithmetic crossover are employed to solve the optimization problem of the dimensional synthesis of the five-point double-toggle mold clamping mechanism with the performance of thrust saving for the prescribed input and output strokes [20]. GA is commonly used to optimize equipment designs, including T-tail structures, crane legs, layered mesh reinforced cylindrical shells, sandwich structure T joints, and satellite separation systems [21][22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%