Nothing Special   »   [go: up one dir, main page]
Welcome to the InfoSci Platform
Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm

Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm

Baorong Qiu
Copyright: © 2024 |Volume: 26 |Issue: 1 |Pages: 20
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9798369323878|DOI: 10.4018/JCIT.349738
Cite Article Cite Article

MLA

Qiu, Baorong. "Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm." JCIT vol.26, no.1 2024: pp.1-20. http://doi.org/10.4018/JCIT.349738

APA

Qiu, B. (2024). Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm. Journal of Cases on Information Technology (JCIT), 26(1), 1-20. http://doi.org/10.4018/JCIT.349738

Chicago

Qiu, Baorong. "Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm," Journal of Cases on Information Technology (JCIT) 26, no.1: 1-20. http://doi.org/10.4018/JCIT.349738

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The traditional entrepreneurial resource decision-making model that relies on empirical decision-making or simple template matching is difficult to adapt to the current complex social environment. Therefore, the multi-objective grey wolf algorithm (MOGWO) is used to solve the Pareto frontier of the problem model, replacing the optimal solution with the optimal solution set, and then selecting the optimal scheduling plan according to the actual situation, so as to make the decision-making plan more scientific and reasonable. In order to optimize this algorithm, two improvement strategies are proposed on the basis of analysing the movement of individual grey wolves. The research in this paper provides an important reference for the machine learning algorithm and the improved multi-objective grey wolf algorithm. The experimental results show that the MOGWO algorithm can overcome the shortcomings of the basic grey wolf algorithm (GWO) in terms of insufficient exploratory ability and local convergence, and has higher search efficiency, better optimality finding ability and stability.