Research Article
Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm
@INPROCEEDINGS{10.4108/eai.17-11-2023.2342782, author={Lei Sun}, title={Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm}, proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India}, publisher={EAI}, proceedings_a={ICSETPSD}, year={2024}, month={1}, keywords={engineering bidding system tcm-knn genetic algorithm enterprise credit evaluation}, doi={10.4108/eai.17-11-2023.2342782} }- Lei Sun
Year: 2024
Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm
ICSETPSD
EAI
DOI: 10.4108/eai.17-11-2023.2342782
Abstract
Project bidding is a widely used method of project transactions in the world. It promotes fairness and justice in the field of project construction, and also prevents and overcomes corruption in the field of project construction. Nowadays, electronic bidding has gradually replaced traditional bidding methods, and the big date (BD) brought by electronic bidding has also provided sufficient convenience for the establishment and improvement of engineering enterprise credit evaluation systems. Therefore, this paper is to solve the problem of insufficient application of BD in the current engineering electronic bidding. This paper combines the TCM-KNN classification algorithm with the genetic algorithm(GA), collects data information related to bidding transactions from multiple channels, and conducts research on the internal correlation and mutual influence between these data information, so as to determine the evaluation indicators suitable for engineering enterprise credit evaluation. And on the basic of the BD analysis of engineering electronic bidding, this paper establishes the corresponding engineering enterprise credit evaluation(ECE) index system and ECE model. Finally, the experimental results show that the GA is used to optimize the TCM-KNN model, and the parameters that are more in line with the actual situation are obtained.
