[1]卢万杰,陈子林,付华,等.多策略融合的改进黏菌算法及其应用[J].智能系统学报,2023,18(5):1060-1069.[doi:10.11992/tis.202206015]
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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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]
多策略融合的改进黏菌算法及其应用
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第5期
页码:
1060-1069
栏目:
学术论文—机器学习
出版日期:
2023-09-05
- Title:
- Improved slime mould algorithm with multistrategy integration and its application
- Keywords:
- intelligent optimization algorithm; slime mould algorithm; sparrow search algorithm; multistrategy integration; improved slime mould algorithm; XGBoost; transformer fault diagnosis; benchmark function
- 分类号:
- TP18；TM407
- 摘要:
- 针对黏菌算法存在自适应能力有限，抗停滞能力弱等不足，提出多策略融合的改进黏菌算法。采用Bernoulli混沌初始化，丰富种群多样性，提升算法优化精度和收敛速度；提出动态非线性递减策略，动态调节黏菌个体探索幅度，协调并优化算法全局搜索与局部开发能力；结合麻雀算法的预警机制与折射反向学习策略，优化黏菌个体分离觅食过程，防止前期优质个体流失以及后期种群多样性匮乏，提升算法整体抗停滞能力。通过对基准测试函数及部分CEC2017测试函数进行寻优对比实验，测试结果表明改进算法具有更好的寻优精度、稳定性。利用改进算法优化XGBoost参数并将其用于变压器故障诊断，进一步验证了改进策略的有效性及算法的工程实用性。
- 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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备注/Memo
收稿日期:2022-6-14。
基金项目:国家自然科学基金项目(51974151，71771111)；辽宁省高等学校国(境)外培养项目(2019GJWZD002)；辽宁省高等学校创新团队项目(LT2019007)；辽宁省教育厅科技项目(LJ2019QL015)；辽宁省高等学校基本科研项目(LJKZ0352).
作者简介:卢万杰，副教授，主要研究方向为机电一体化、人工智能控制、设备故障诊断。主持参与国家自然科学基金、辽宁省自然科学基金指导项目等5项;陈子林，硕士研究生，主要研究方向为智能优化算法、变压器故障诊断;付华，教授，博士生导师，主要研究方向为智能检测与智能控制、供电系统故障辨识、远程监测监控技术。主持国家自然科学基金、教育部博士点基金等20余项，出版专著5部，发表学术论文190余篇
通讯作者:卢万杰.E-mail：luwanjie0912@126.com
更新日期/Last Update:
1900-01-01