Title: Prediction of environmental factors in seafood cold chain transportation based on IAGA-ELM algorithm

Authors: Xu E; Yujia Jin; Yinong Chen; Shenghui Zhao; Yang Wang

Addresses: School of Information Science and Technology, Bohai University, Jinzhou, 121013, Liaoning Province, China; School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, Anhui, China ' School of Information Science and Technology, Bohai University, Jinzhou, 121013, Liaoning, China ' School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA ' School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, Anhui, China ' School of Computer and Information Engineering, Chuzhou University, Chuzhou, 239000, Anhui, China

Abstract: Cold chain transportation is often used to ensure the quality of seafood. In order to reduce the impact of environmental factors on product quality during cold chain transportation, an environmental factor prediction method based on improved adaptive genetic algorithm (IAGA) and extreme learning machine (ELM) is developed to predict the environmental factors generated in cold chain transportation. The environmental factor data composed of the gases released in the decay mechanism of cold fresh seafood is collected for training ELM with fast training speed and strong generalisation ability. Furthermore, the prediction model of environmental factors in the process of seafood cold chain transportation is established. Because initial connection weights and threshold values of traditional ELM have the characteristics of randomness, IAGA is used to address this issue. Compared with the existing ELM, GA-ELM, AGA-ELM and other models, our experimental results show that the proposed model obtains higher prediction accuracy and lower error rate.

Keywords: improved adaptive genetic algorithm; IAGA; extreme learning machine; ELM; cold chain transportation; environmental factors; prediction.

DOI: 10.1504/IJSPM.2023.136916

International Journal of Simulation and Process Modelling, 2023 Vol.20 No.3, pp.159 - 171

Received: 04 Sep 2021
Accepted: 24 Feb 2022
Published online: 29 Feb 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article