TY - JOUR
KW - Algorithm convergence
KW - Algorithms
KW - Average values
KW - Commerce
KW - Condition
KW - Intangible cultural heritages
KW - Living fossils
KW - Marketing
KW - National cultures
KW - PSO algorithms
KW - Particle swarm algorithm
KW - Particle swarm optimization (PSO)
KW - Particle swarm optimization algorithm
KW - Sphere functions
KW - algorithm
KW - Marketing
AU - Cenxi Li
AU - Boya Liu
AB - Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world s intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm s convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed.
DO - 10.1155/2022/6679237
N1 - Publisher: Hindawi Limited
N2 - Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world s intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm s convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed.
TI - Marketing Archive Management of Drama Intangible Cultural Heritage Based on Particle Swarm Algorithm
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134538662&doi=10.1155%2f2022%2f6679237&partnerID=40&md5=7abf34a20192741a710c717a184d95e7
VL - 2022
SN - 16875265 (ISSN)
ER -