Abstract
Sports industry cluster refers to the economic phenomenon that sports related enterprises gather in a large number in a specific area. For the sports enterprises in the cluster, they can obtain huge competitive advantages through enterprise agglomeration, thus obtaining better development and rich economic benefits. The optimization of particle swarm optimization is interlinked with the agglomeration of industrial clusters. Therefore, in view of the limitation of the standard particle swarm optimization (PSO) algorithm, an improved particle swarm optimization algorithm-diaphragm particle swarm optimization (D-PSO) was proposed and used to simulate the formation of sports industry clusters. D-PSO introduces the cell membrane processing mechanism of the biological system into the PSO algorithm, which improves the ability of the PSO algorithm to get rid of local extremum points. The competitiveness value of the sports industry cluster is the value of the objective function solved by the D-PSO algorithm. The geographical coordinates of the industrial cluster were the locations in the particle search space of the D-PSO algorithm. The D-PSO algorithm is used to simulate the aggregation process of enterprises in the cluster. Compared with the standard PSO, the D-PSO algorithm has better convergence performance and optimal rate. The results of case analysis show that the proposed method can effectively predict the development trend of sports industrial clusters.
Highlights
Industrial cluster is the mainstream trend of modern industrial development and the main driving force of industrial upgrading
Particle swarm optimization is interlinked with the agglomeration of sports industry clusters. e formation of sports cluster is a self-organizing process. e particle swarm optimization algorithm is a self-organizing algorithm [17,18,19], and its optimization process is selforganizing
Erefore, in order to use the PSO algorithm to simulate the formation of industrial clusters, we use particle swarm optimization to solve the problem. e competitiveness value of the industrial cluster is the value of the objective function solved by the PSO algorithm. e geographical coordinates of the industrial cluster are the position in the particle search space in the PSO algorithm. ere must be “cooperation” and “competition” between enterprises within the cluster, which can be achieved through the “self-cognition” part and “society” part of the PSO algorithm
Summary
Industrial cluster is the mainstream trend of modern industrial development and the main driving force of industrial upgrading. Industrial clusters play an important role in promoting regional industrial development and enhancing regional competitiveness. Industrial cluster is an economic phenomenon based on self-organization structure. Sports industry cluster has become a hot field of academic research; many researchers will be full of research enthusiasm into it and has made many scientific research results. Particle swarm optimization is interlinked with the agglomeration of sports industry clusters. E formation of sports cluster is a self-organizing process. It can be seen that the optimization of particle swarm and the agglomeration of sports industry cluster are interlinked. Erefore, this paper makes a detailed analysis of industrial clusters based on self-organization and PSO algorithm. An improved PSO algorithm-diaphragm particle swarm optimization (D-PSO) is proposed to accurately solve the evolution and formation mechanism of industrial clusters. Studying industrial clusters from the perspective of self-organization provides a new analytical thinking for academic research of industrial clusters
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
