We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
The structural weight design method based on the modified grasshopper optimization algorithm.
- Authors
Ye, Yin; Xiong, Shengwu; Dong, Chen; Chen, Zhenyi
- 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.
- Subjects
STRUCTURAL design; MATHEMATICAL optimization; SPEED reducers; GRASSHOPPERS; SEARCH algorithms; PRESSURE vessels; SWARM intelligence; BIOLOGICALLY inspired computing
- Publication
Multimedia Tools & Applications, 2022, Vol 81, Issue 21, p29977
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-022-12562-3