Multi-Objective Optimization for Football Team Member Selection

Team composition is one of the most important and challenging directions in the recommendation problem. Compared with a single person, the advantage of a team is mainly reflected in the synergy of team members' complementary collaboration. To build a high-efficiency team, how to choose the team...

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Bibliographic Details
Published inIEEE access Vol. 9; pp. 90475 - 90487
Main Authors Zhao, Haoyu, Chen, Haihui, Yu, Shenbao, Chen, Bilian
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Analytical models
Composition
Constraint modelling
Data analysis
Evolutionary algorithms
Football
Games
genetic algorithm
Genetic algorithms
Linear programming
multi-objective optimization
Multiple objective analysis
Optimization
Sports
Team composition
Teamwork
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Summary:Team composition is one of the most important and challenging directions in the recommendation problem. Compared with a single person, the advantage of a team is mainly reflected in the synergy of team members' complementary collaboration. To build a high-efficiency team, how to choose the team members has become a tricky problem. However, there is a lack of quantitative algorithms and validation methods for team member selection. In this paper, we put forward three indicators to measure a team's ability and formulate the selection of football team members as a multi-objective optimization problem. Subsequently, an evolutionary player selection algorithm based on the genetic algorithm is proposed to solve the team composition problem. We verify the effectiveness of the team member recommendation algorithm via data analysis, football game simulation under different budget constraints and provide comparisons with existing methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3091185