[1]LU Wanjie,CHEN Zilin,FU Hua,et al.Improved slime mould algorithm with multistrategy integration and its application[J].CAAI Transactions on Intelligent Systems,2023,18(5):1060-1069.[doi:10.11992/tis.202206015]
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Improved slime mould algorithm with multistrategy integration and its application

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 1060-1069 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Improved slime mould algorithm with multistrategy integration and its application
Author(s):
LU Wanjie1;  CHEN Zilin2;  FU Hua2;  WANG Zhizhong2;  WANG Jiuyang3
1. School of Mechanical Engineering, Liaoning Technical University, Fuxin 125000, China;
2. School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China;
3. Huludao Power Supply Company, State Grid, Huludao 125000, China
Keywords:
intelligent optimization algorithm; slime mould algorithm; sparrow search algorithm; multistrategy integration; improved slime mould algorithm; XGBoost; transformer fault diagnosis; benchmark function
CLC:
TP18；TM407
DOI:
10.11992/tis.202206015
Abstract:
In order to address the disadvantages of the slime mold algorithm, such as limited adaptive ability and weak antistagnation capability, we propose an improved slime mold algorithm with multistrategy integration. Bernoulli chaos is used to generate the initial population, which enriches the population diversity, improves the optimization accuracy, and enhances the convergence speed of the algorithm. A dynamic nonlinear decreasing strategy is proposed to adjust the exploration range of slime mold individuals dynamically; this strategy coordinates and optimizes the global exploration and local exploitation capabilities of the algorithm. By combining the early warning mechanism of the sparrow search algorithm and refracted opposition-based learning, the foraging process of slime mold individuals is optimized, preventing the loss of high-quality individuals in the early stage and ensuring population diversity in the later stage, thereby improving the overall antistagnation capability of the algorithm. Comparative optimization experiments are conducted on benchmark functions and some CEC2017 test functions, demonstrating that the improved algorithm exhibits superior optimization accuracy and stability. Furthermore, the improved algorithm is used to optimize XGBoost parameters and is applied to transformer fault diagnosis, which further verifies the effectiveness of the improved strategy and the practicality of the algorithm in engineering applications.
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