Name
Title | ||
|---|---|---|
The structural weight design method based on the modified grasshopper optimization algorithm |
Abstract | ||
|---|---|---|
Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solve the NP problem. Grasshopper optimization algorithm (GOA) is a nature-inspired algorithm that mimics the swarming behaviors of grasshopper insects, but the original GOA has two main problems: the convergence rate is slow and the convergence accuracy is poor. We propose a novel grasshopper optimization algorithm (CV-GOA) consisting of chaos strategy and velocity perturbation mechanism to improve the performance of standard GOA. In CV-GOA, the initial artificial swarm is constructed by Logistic map to increase the diversity of the population and improve the feasibility of finding the global optimal solution; then a set of the velocity vector is introduced and the velocity perturbation mechanism is used to update the velocity of grasshoppers and disturbs the position of grasshoppers, it can improve the searching speed of the algorithm and help the algorithm jump out of the local optimal trap, and improve the optimization accuracy of the algorithm. Experiments are conducted on fifteen benchmark functions to test the accuracy and convergence rate of CV-GOA. Experiments show the proposed CV-GOA achieves higher precision and better convergence rate than other variants. In addition, three structural weight design problems are optimized by CV-GOA, they are cantilever beam design problem, pressure vessel design problem and speed reducer design problem. The results indicate structural weight is designed with superiority. It also proves the effectiveness and value of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11042-022-12562-3 | Multimedia Tools and Applications |
Keywords | DocType | Volume |
Structural weight design, Grasshopper optimization algorithm, Function optimization, Intelligent computing | Journal | 81 |
Issue | ISSN | Citations |
21 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 28 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Ye | 1 | 0 | 0.34 |
| Shengwu Xiong | 2 | 189 | 53.59 |
| Chen Dong | 3 | 1 | 1.70 |
| Zhenyi Chen | 4 | 10 | 3.80 |