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Product quality prediction based on RBF optimized by firefly algorithm 被引量:1
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作者 HAN Huihui WANG Jian +1 位作者 CHEN Sen YAN Manting 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2024年第1期105-117,共13页
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred... With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality. 展开更多
关键词 product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm
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An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems
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作者 L. Q. Zhang J. Xiong J. K. Liu 《Journal of Applied Mathematics and Physics》 2023年第11期3641-3650,共10页
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ... In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance. 展开更多
关键词 swarm intelligent optimization algorithm Fruit Fly optimization algorithm Adaptive Step Local Optimum Convergence Speed
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Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms
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作者 Zhonghai Jiang Qian Wang +1 位作者 Liangbing Zhou Chun Xiao 《Fluid Dynamics & Materials Processing》 EI 2023年第6期1693-1708,共16页
A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.... A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model. 展开更多
关键词 Asphalt pavement temperature field swarm intelligence optimization algorithm PREDICTION
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