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| Improved Sparrow Search Algorithm Based on Multi-strategy Fusion |
| WANG Ronglin 1**,WANG Haibo 1*,,LI Zhifeng 2***,LI Pengtao 2** , WEN Hao1**, LIU Chunjie 1**
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1 School of Mechanical and Electrical Engineering, Jilin Institute of Chemical Technology, Jilin City 132022,China
2 School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin City, 132022,China;
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Abstract Aiming at the problems of slow convergence speed, insufficient exploration ability and easy to fall into local optimum of sparrow search algorithm (SSA), an improved sparrow search algorithm (OSSSA) based on multi-strategy fusion is proposed. Firstly, the diversity of population is initialized with the help of Tent chaotic map to improve the quality of initial solution; Secondly, the first stage exploration strategy of osprey algorithm is introduced in the location update of discoverer to improve the exploration ability of population to local search; Finally, cauchy mutation and variable spiral search strategy are introduced to update the follower position to improve the search efficiency and global search performance of the algorithm, reduce the probability of the algorithm falling into the local optimal solution and enhance the global optimization ability of the algorithm. On this basis, eight benchmark functions are simulated to evaluate the optimization performance of the algorithm. Through the analysis of simulated images and data, the improved sparrow search algorithm has greatly improved the convergence speed and optimization accuracy, which verifies the effectiveness of the improved strategy.
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Published: 25 March 2024
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| [1] |
CHEN Na, KONG Fanxing, WANG Yanxu, HE Tengfei, LI Shengnan. Turning Tool Wear Detection Method Based on EfficientNetV2[J]. Journal of Jilin Institute of Chemical Technology, 2024, 41(3): 21-24. |
| [2] |
CHEN Na , KONG Fanxing, WANG Yanxu, HE Tengfei, LI Shengnan.
Research on Turning Tool Wear based on Convolutional Neural Network
[J]. Journal of Jilin Institute of Chemical Technology, 2023, 40(9): 43-47. |
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