Title: | Online Labor Education Optimization Method Based on Computer Intelligent Algorithm |
Author(s): | Huang L; Zheng T; Huang Q; |
Address: | "School of Marxism, Shanghai Lixin University of Accounting and Finance, Pudong 201209, Shanghai, China. School of Journalism and Communication, Shanghai University, Baoshan 200444, Shanghai, China. School of Architecture and Urban Planning, Tongji University, Yangpu 200092, Shanghai, China. School of Public Management and Services, Shanghai Urban Construction Vocational College, Fenxian 201415, Shanghai, China" |
ISSN/ISBN: | 1687-5273 (Electronic) 1687-5265 (Print) |
Abstract: | "People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education" |
Keywords: | "*Algorithms Computer Simulation Computers Humans *Models, Theoretical Pheromones;" |
Notes: | "MedlineHuang, Liming Zheng, Tingting Huang, Qiaomin eng Retracted Publication 2022/08/30 Comput Intell Neurosci. 2022 Aug 17; 2022:8740978. doi: 10.1155/2022/8740978. eCollection 2022" |