{"title":"基于改进的多目标灰狼算法的大学生创业路径优化方法","authors":"Baorong Qiu","doi":"10.4018/jcit.349738","DOIUrl":null,"url":null,"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.","PeriodicalId":43384,"journal":{"name":"Journal of Cases on Information Technology","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm\",\"authors\":\"Baorong Qiu\",\"doi\":\"10.4018/jcit.349738\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":43384,\"journal\":{\"name\":\"Journal of Cases on Information Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cases on Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/jcit.349738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cases on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/jcit.349738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Optimization Method of College Students' Entrepreneurial Path Based on Improved Multi-Objective Gray Wolf Algorithm
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.
期刊介绍:
JCIT documents comprehensive, real-life cases based on individual, organizational and societal experiences related to the utilization and management of information technology. Cases published in JCIT deal with a wide variety of organizations such as businesses, government organizations, educational institutions, libraries, non-profit organizations. Additionally, cases published in JCIT report not only successful utilization of IT applications, but also failures and mismanagement of IT resources and applications.