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The structural weight design method based on the modified grasshopper optimization algorithm

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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.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 62176194 and Grant 62101393, in part by the Major Project of IoV under Grant 2020AAA001, in part by the Sanya Science and Education Innovation Park of the Wuhan University of Technology under Grant 2021KF0031 and Grant HSPHDSRF-2022-03-017, in part by the National Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX1148, and in part by the Open Project of the Wuhan University of Technology Chongqing Research Institute under Grant ZL2021-6, and in part by the Natural Science Foundation of Fujian Province under Grant 2020J01500. We thank MindSpore for the partial support of this work, which is a new computing framework https://www.mindspore.cn/.

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China

    Yin Ye & Shengwu Xiong

  2. Sanya Science and Education Innovation Park of Wuhan University of Technology, Sanya, 572000, China

    Yin Ye & Shengwu Xiong

  3. College of Computer Science and Big Data, Fuzhou University, Fuzhou, 350116, China

    Chen Dong

  4. Department of Electrical Engineering, University of South Florida, Tampa, FL, 33620, USA

    Zhenyi Chen

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  1. Yin Ye
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Correspondence to Shengwu Xiong or Chen Dong.

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Appendix: The nomenclature section

Appendix: The nomenclature section

Table 8 Description of the variables and abbreviations
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Ye, Y., Xiong, S., Dong, C. et al. The structural weight design method based on the modified grasshopper optimization algorithm. Multimed Tools Appl 81, 29977–30005 (2022). https://doi.org/10.1007/s11042-022-12562-3

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