Exploration strategies for balancing efficiency and comprehensibility in model checking with ant colony optimization

Kumazawa, Tsutomu; Takimoto, Munehiro; Kambayashi, Yahiko

doi:10.1080/24751839.2022.2047470

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Nature-derived algorithms are the most powerful algorithms for optimization. Recently, swarm intelligence [26], the biologically inspired algorithms, have been studied dramatically, such as firefly swarm optimization algorithm [14], ant colony optimization technique [27], particle swarm optimization (PSO) algorithm [28], artificial fish swarm optimization algorithm [29], and swallow swarm optimization (SSO) [30]. They have been applied to solve various problems, such as healthcare, finance, energy, image thresholding, and others.…”

Section: Swarm Intelligence Optimization Algorithmmentioning

confidence: 99%

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

Han,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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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.

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