scholarly journals Method for Forecasting Urban National Sports and Fitness Demand Based on Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jianhui Wu
Keyword(s):  
Ant Colony Algorithm ◽  
Supply And Demand ◽  
Social Economy ◽  
Paper Analysis ◽  
Social Needs ◽  
Ant Colony ◽  
Predictive Analysis ◽  
Urban Residents ◽  
Sports Facilities ◽  
Rational Allocation

With the continuous development of social economy, when people are pursuing economic income, they are also gradually paying attention to their own physical health. They achieve their own physical exercise through sports such as running, fitness, and mountaineering, but these sports often require a certain venue and equipment. Therefore, in view of these sports fitness demands, the ant colony algorithm is introduced to sort out the fitness activities in the context of urban residents’ supply and demand relationships, analyze the demand from both subjective and objective aspects, and explore the lack of supply of sports facilities in this paper. Analysis is conducted from cognitive and national fitness, social needs, habits, and other perspectives. It tries to guide the rational allocation and creation of resources, obtain residents’ fitness awareness and support, and provide corresponding suggestions and support for residents’ fitness activities. The simulation experimental results show that the ant colony algorithm is effective and can support the predictive analysis of the urban national fitness demand.

scholarly journals Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Zhang ◽  
Weibo Sun ◽  
Sang-Bing Tsai
Keyword(s):  
Partial Order ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Order Relation ◽  
Ant Colony ◽  
Route Optimization ◽  
Sports Facilities ◽  
Routing Method ◽  
Evacuation Plan ◽  
Evacuation Route

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

Data association method of SLAM based on ant colony algorithm

2009 ◽  
Vol 29 (1) ◽  
pp. 136-138 ◽  
Author(s):  
Wen-jing ZENG ◽  
Tie-dong ZHANG ◽  
Yu-ru XU ◽  
Da-peng JIANG
Keyword(s):  
Ant Colony Algorithm ◽  
Data Association ◽  
Ant Colony ◽  
Association Method
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