Solve the IRP problem with an improved discrete differential evolution algorithm

The development of a country’s language and literature is the orientation of the country’s spiritual strength, and it is also the embodiment of the national literary quality. Chinese language and literature has a very long history of development, the content of which is broad and deep, and there are countless literary works. It is impossible for one person to fully understand these works after spending his entire life. As a result, these literary resources are optimized and constructed and classified into various disciplinary contents, so that students can systematically learn literary knowledge according to their own interests. Therefore, this paper conducts a disciplinary analysis of the construction of Chinese language and literature resources by integrating the method of fuzzy mathematics. Through the study of fuzzy mathematics, the method of fuzzy cluster analysis is proposed to classify Chinese language and literature resources, and the method of fuzzy comprehensive evaluation is used to evaluate the contents of various subjects. This paper selected the richness of subject content, faculty strength, talent demand, and subject difficulty as evaluation indicators. By comparing the scores of various discipline indicators before and after resource optimization, the results showed that using fuzzy cluster analysis, disciplines 1 and 2 were popular disciplines. Disciplines 4 and 5 were general disciplines, and disciplines 3 and 4 were unpopular disciplines. Using the fuzzy comprehensive average, the comprehensive scores of the six disciplines were 84, 79, 80, 82, 83, and 75, respectively. The quality of resource construction in these disciplines was high. In addition, the scores given by experts were all above 70 points, with an average score of about 83 points. It showed that the method of using fuzzy mathematics to construct Chinese language and literature resources has achieved good results, and the results obtained by using the method of disciplinary analysis were reliable and feasible.

Section: Language and Literature Resources' Constructionmentioning

confidence: 99%

Disciplinary Construction of Chinese Language and Literature Resources Integrating Fuzzy Mathematics

2022

“…rough the survey, the frequency of occurrence of passenger information (housing, transportation, shopping, scenery, food, entertainment) in January 2016 was as follows: 600, 1000, 1650, 3050, 2050, and 2750. en, the total frequency of passenger information is calculated by formula (1) to obtain the total frequency A � 11100. Using formula (6) to find the predicted value of the ordinary average algorithm is V 1 � 3, and the weights for passenger information are as follows: 0.054, 0.090, 0.149, 0.275, 0.185, 0.248. Using formula (5) to calculate the predicted value of the weighted average algorithm is V � 4. e forecast of the cloud service average way of the common average shown is to strengthen the collection of traveler shopping information, while the cloud service prediction by adding weights is to strengthen the information collection of tourists' food.…”

Section: Passenger Informationmentioning

confidence: 99%

“…en, the total frequency of tourism resource information is calculated to get A � 9600. Using formula (6) to find the predicted value of the ordinary average algorithm is V 1 � 3, and the weights for the tourism resource information are 0.063, 0.083, 0.125, 0.146, 0.167, and 0.417. Using formula (5) to calculate the predicted value of the weighted average algorithm is V � 5. e predictions of cloud services based on the common average algorithm shown are building resources, while the predictions of cloud services by adding weights are geomorphic resources.…”

Section: Travel Resourcesmentioning

confidence: 99%

“…e weighted average algorithm has simple and highly accurate predictive analysis capabilities, and many researchers have studied the application of the weighted average algorithm to cloud services in smart tourism management. Among them, Cheng S and Wang Z research found that the weighted average algorithm can cooperate with cloud services to analyze and predict tourism information in smart tourism management [6]. Wu et al found that the cloud service of the weighted average algorithm can improve the processing and analysis ability of tourism data, so as to improve the efficiency of tourism management [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Cloud Service in Smart Tourism Management Based on Weighted Average Algorithm

Feng

2022

Tourism has become the main force driving urban economic development. However, there are problems such as low utilization rate of tourism resources and inconsistency of tourism information. The new smart tourism management model can sense tourism resources, tourist information, tourism development, and other information in a timely manner through terminal devices such as the Internet. The core of smart tourism management is to obtain travel information of travelers through cloud service technology, organize travel information, and recommend personalized travel plans for travelers. Apply cloud services to smart tourism management, and use the weighted average algorithm to make regression predictions on passenger information. This paper compares and analyzes the application of cloud service based on the algorithm for calculating the weighted average and the application of ordinary cloud service in smart tourism management through experiments. The experimental results show that the cloud service by adding weights can improve the forecast of the traveler’s information by 36% and 22%, respectively, compared with the cloud service of the ordinary average algorithm. Also, the effect of tourists’ travel experience is 72.2% and 56.3%, respectively. In the forecast of tourism information in tourism resources, tourism economy, and tourism activities, cloud services by adding weights maintain stable economic growth and improve tourists’ tourism experience. The cloud service in the smart tourism management of the weighted average algorithm can formulate corresponding tourism marketing strategies through the integration and prediction of tourism information, enhance the growth of the tourism economy, and improve the tourism experience of tourists.

“…e discovery of GA provides a simple solution to complex data problems. It has superior ability to deal with complex functions, programs, system structures, and other issues [14].…”

Section: Gamentioning

confidence: 99%

Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm

Haimeng

2022

With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of extremely complex data in the study of bioinformatics. Analyzing data in bioinformatics research requires accurate calculation by computer algorithms. However, common computer algorithms such as the dynamic programming algorithm and the genetic algorithm have the disadvantages of large memory or inaccurate optimization. Combining the ant colony algorithm (ACA) and GA can give the advantages of the two methods that should be fully utilized to efficiently analyze the biological information data. In this paper, the ant colony fusion genetic algorithm (ACA-GA), GA, and dynamic programming algorithm are used to compare and analyze the sensitivity, convergence speed, sequence alignment accuracy, and required memory space of gene sequences. The experimental results show that compared with ACA-GA, the dynamic programming algorithm has the advantage of finding the optimal alignment of 100%, but the memory required is too large, the memory required is more than 10 times that of the ant colony fusion genetic algorithm and the sensitivity is not as good as ACA-GA. The convergence speed of ACA-GA is faster than the gene comparison speed of GA and the accuracy is 2.6% better than that of GA on average. ACA-GA has the advantages of GA and ACA, which can improve the computational efficiency of biological data in bioinformatics.