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Method for Forecasting Urban National Sports and Fitness Demand Based on Ant Colony Algorithm
Author(s) -
WU Jian-hui
Publication year - 2021
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2021/5917756
Subject(s) - context (archaeology) , computer science , physical fitness , supply and demand , ant colony optimization algorithms , psychology , operations research , artificial intelligence , economics , medicine , microeconomics , engineering , paleontology , biology , physical therapy
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.

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