Current Situation and Strategy of Sports Equipment Procurement Based on Improved Ant Colony Optimization Algorithm

With the development of the Internet, more and more sports equipment has entered people's lives. However, due to the late start of sports venue procurement work and the lack of corresponding theoretical knowledge and management experience, there are many problems in its practical application. This article is based on the improved ant colony optimization algorithm and combines traditional heuristic search and random programming to explore a new procurement model for sports equipment in universities based on the improved ant colony optimization algorithm. It introduces new methods and applies them to practical applications. When considering factors such as genetic operations and simulated annealing, analysis and calculation were conducted. In the solving process, this article uses the particle swarm evolutionary optimization model to set parameters and design constraint conditions for it. In this paper, Tabu search strategy is combined to achieve the optimal solution, and heuristic search and random sexual selection are combined to design a procurement platform, and the platform is functional tested. The test results show that the automation level of the platform is over 90%. Improved ant colony optimization algorithms can automatically complete some optimization tasks during user registration and login processes, such as automatically detecting the legitimacy of user input information, thereby improving user experience and procurement efficiency.