Application of DE-GWO-SVM Algorithm in Business Order Prediction Model

Liu, Jing; Zhu, Xiaojun; Zhang, Yanfeng

doi:10.1109/icsess49938.2020.9237714

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once they send a warning signal, the population needs to escape immediately and move to another safe place for food [52]. The basic parameter setting in this algorithm is as follow: (iii) GWO By analyzing and summarizing the population mechanism of wolves, Mirjalili S proposed the grey wolf optimizer algorithm in 2013 [53][54][55]. Similar to other natureinspired heuristic algorithms, the GWO also starts with a set of randomly generated locations.…”

Section: Svm Optimized By Multiple Heuristic Algorithmsmentioning

confidence: 99%

Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Zhong

2023

Sustainability

View full text Add to dashboard Cite

Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.

show abstract