Sports Competition System Arrangement Based on an Improved Multi-Objective Optimization Algorithm

Abstract

This research suggests a flexible scheduling method for professional athletic events that hybridizes the tabu search with the genetic algorithms, resulting in a significant improvement in the efficiency of traditional game scheduling game-match planning outcomes. This project aims to lower the travel expenses for all participating teams. As a starting point for the experiment, data from well-known sports leagues (such as Major League Baseball and the National Basketball Association) has been utilized. The new strategy more effectively identifies superior outcomes than previous methods. Apart from devising a workable plan that satisfies all scheduling constraints, the challenge tackled in this paper is further complicated by the need to minimize travel expenses and ensure that each club plays an equal number of home games. To overcome the difficult challenge, the authors describe the issue of scheduling as a matter of optimization and use the idea of evolutionary strategy, taking into account sequential occurrences in a socially connected environment.