Particle swarm optimization for optimal design of experiments in quantile regression

Chen Xing

doi:10.1117/12.2686163

28 July 2023 Particle swarm optimization for optimal design of experiments in quantile regression

Chen Xing

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564I (2023) https://doi.org/10.1117/12.2686163
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

The nonlinear quantile regression model has been widely applied in various fields such as drug development, medical research, and industrial production. However, due to the non-convexity of its objective function, a universally applicable optimization design for the quantile regression model has not been developed so far. Although a few existing studies have made some progress, these studies can only be realized under some special mathematical assumptions. In practice, experimental designs often cannot meet these assumptions, making it difficult to apply these research results in actual experiments. Therefore, we need to find a new method to solve this problem. In this paper, based on the idea of particle swarm optimization algorithm, we have successfully solved the optimal experimental design problem of nonlinear quantile regression model. Unlike traditional experimental design methods, we can find the optimal solution without any mathematical assumptions, which greatly enhances the universality of the quantile regression model. This research achievement has important guiding significance for designing effective quantile regression experiments in practical experimental design and can provide effective reference for related fields' practical work.

Citation Download Citation

Chen Xing "Particle swarm optimization for optimal design of experiments in quantile regression", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564I (28 July 2023); https://doi.org/10.1117/12.2686163

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KEYWORDS

Design and modelling

Particle swarm optimization

Mathematical modeling

Particles

Nonlinear optimization

Mathematical optimization

Algorithm development

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