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- W3027166811 abstract "Grasshopper optimization algorithm is one of the latest swarm intelligence algorithms, however it is easy to fall into the local optimum and has the defect of poor search ability. Therefore, Gaussian and chaos theory are combined to improve the original grasshopper optimization algorithm. Firstly, the Sinusoidal chaotic map is used to initialize the solution space of the population; then, Gaussian and Logistic chaotic map are used to improve the position update formula of grasshopper optimization algorithm; finally, eight benchmark numerical test functions are used to verify and test the search performance of this algorithm with ALO, PSO, GOA. It can be seen from the experimental data that the improved grasshopper optimization algorithm has better convergence accuracy and stability than the other three swarm intelligent algorithms. Besides, GC-GOA has good performance in the design of three-bar truss problem." @default.
- W3027166811 created "2020-05-29" @default.
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- W3027166811 date "2019-10-01" @default.
- W3027166811 modified "2023-09-23" @default.
- W3027166811 title "Grasshopper optimization algorithm combining Gaussian and chaos theory for optimization design" @default.
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- W3027166811 doi "https://doi.org/10.1109/eitce47263.2019.9094989" @default.
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