Abstract
An interactive swarm intelligence algorithm based on master-slave Gaussian surrogate model (ISIA-MSGSM) is proposed in this paper. In the algorithm, particle swarm optimization is used to act on the optimization search. During the search process, some data are sampled dynamically from the searching swarm to build the master and the slave Gaussian surrogate model, and all the particles will go through interactive evaluations based on the two kinds of surrogate models and the accurate model, which can reduce the computation cost of the objective function. At the same time, the surrogate models are managed dynamically guided by the accurate model to ensure the computational accuracy. Through the dynamical update to the master and slave model, the balance between the global exploration and the local exploitation is ensured which contributes to the efficiency of the algorithm. The experiment results on benchmark problems show this method not only can decrease the computation cost, but also has good robustness with a satisfied optimization performance.
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Acknowledgments
This work was supported in part by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Information (No.U1609214), National Natural Science Foundation of China (No.61203371), Zhejiang Provincial Natural Science Foundation (No.LQ16F030002), and Zhejiang Provincial New seedling talent program (No. 0201310H35).
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Jie, J., Zhang, L., Zheng, H., Zhou, L., Shan, S. (2018). Interactive Swarm Intelligence Algorithm Based on Master-Slave Gaussian Surrogate Model. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_70
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DOI: https://doi.org/10.1007/978-3-319-95957-3_70
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