The Improvement and Application of Structure Optimization Algorithm Based on Flexible Neural Trees

2009 ◽  
Author(s):  
Qu Shou-ning ◽  
Fu Ai-fang ◽  
Liu Zhao-lian ◽  
Chang Xiao-li
Keyword(s):  
Optimization Algorithm ◽  
Structure Optimization

The Structure Optimization Design for the W-Type Metallic Sealing Ring Based on the MOGA Algorithm

2015 ◽  
Vol 727-728 ◽  
pp. 541-545
Author(s):  
Xiang Yu Ding
Keyword(s):  
Mechanical Properties ◽  
Optimal Design ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Structure Optimization ◽  
Ansys Software ◽  
Sealing Ring ◽  
Structure Optimization Design

This paper uses the ANSYS software to analysis the outer arm opening angel(OAOA) and the outer arm arc angle(OAAA) of W-type metallic sealing ring which can effects on the mechanical properties,obtained in that when the OAOA choose from 1.398°to 14.156 °and the OAAA choose from 30.21° to 59.5 °, the mechanical properties of the sealing ring can satisfy the requirement of use. Then using the MOGA optimization algorithm to optimize the design of W-type metallic sealing ring, and find when the OAOA choose 3.39°and the OAAA choose 32.18°are the optimal design of the W-type metallic sealing ring.

scholarly journals Truss Structure Optimization Based on Improved Chicken Swarm Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yancang Li ◽  
Shiwen Wang ◽  
Muxuan Han
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
Learning Strategy ◽  
Differential Evolution Algorithm ◽  
Structure Optimization ◽  
Weighting Factor ◽  
Truss Structure ◽  
Swarm Optimization ◽  
Learning Factor ◽  
Chicken Swarm Optimization

To improve the efficiency of the structural optimization design in truss calculation, an improved chicken swarm optimization algorithm was proposed for truss structure optimization. The chicken swarm optimization is a novel swarm intelligence algorithm. In the basic chicken swarm optimization algorithm, the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. And the inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search. Finally, the overall individual position of the algorithm was optimized by the differential evolution algorithm. The improved algorithm was tested by multipeak function and applied to the truss simulation experiment. The study provided a new method for the truss structure optimization.

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