National Games Event Arrangement Based On Improved Multi-objective Optimization Algorithm

With the improvement of my country’s comprehensive national strength and the continuous development of sports undertakings,the number and scale of large-scale sports events undertaken in my country have gradually increased,which has led to the traditional manual manual sports competition scheduling operation has been difficult to meet the organizers’preparation for sports events.demand.Large-scale sports events involve many factors,and the various evaluation indicators of the schedule are also relatively complex,which requires computers to realize intelligent and automated sports competition scheduling.The essence of sports competition scheduling problem is to allocate time to each sports competition item reasonably and without conflict under the condition of satisfying the specific constraints in the process of sports competition scheduling.It is a typical multi-objective constraint combination problem.The current intelligent sports competition scheduling solution has the problems of small scale of research objects and limited algorithm performance,and it is difficult to apply to large-scale sports events such as the National Games.Aiming at the specific preparatory problems of the 14th National Games in Shaanxi Province,this paper proposes a relatively comprehensive and complete modeling scheme for the competition scheduling of the National Games and two multi-objective optimization algorithms for the competition scheduling problem of the National Games.The main work of this paper mainly includes the following three parts:1.According to the preparatory work of the 14th National Games of China,this paper conducts a comprehensive mathematical modeling of the sports competition arrangement problem of the 14th National Games,designs 5 related factor variables in the multi-objective scheme,and analyzes it according to the According to the characteristics of the National Games and the needs of the organizers,3 basic constraints,2 hard constraints,and 8 soft constraints were designed.Among them,the basic constraints and hard constraints can be applied to various sports events including the National Games;while the soft constraints constitute the evaluation criteria for the arrangement plan of the National Games.This paper designs a fixed-length real number coding scheme and a population initialization strategy according to the factor variables in the mathematical model,which realizes the comprehensiveness of information expression and the simplicity of program design.2.This paper proposes a high-precision competition scheduling scheme for the National Games based on an improved genetic algorithm and mathematical modeling.Firstly,an improved adaptive simulated binary crossover operator is designed.An adaptive crossover probability based on order optimization is proposed.A multi mutation strategy is proposed,which uses non-uniform mutation in the early stage and polynomial mutation in the late stage.An improved catastrophe strategy is adopted in the iterative process.This paper combines whale algorithm with genetic algorithm,runs whale algorithm in each evolutionary iteration of genetic algorithm,and introduces cosine factor for parameter adaptation.The comparative experiments show that the improved genetic algorithm proposed in this paper has slightly higher complexity and higher accuracy.Compared with the manual final scheme of the National Games,the arrangement scheme proposed in this paper has a slight decrease in the degree of superiority,but the time for writing the manuscript has been significantly reduced.3.This paper proposes a rapid scheduling scheme for the National Games competition based on improved particle swarm optimization and mathematical modeling.In this paper,binary particle swarm optimization algorithm is used,and cosine factor mapping function and non mandatory displacement strategy are designed for it.The multi inertia weight strategy is designed.The nonlinear decreasing time-varying strategy is used in the early stage of iteration,and the chaotic decreasing strategy is used in the late stage of iteration.An extension operator is designed to improve the diversity of particle swarm optimization.The comparative experiments show that the improved particle swarm optimization algorithm proposed in this paper has general accuracy and fast speed.Compared with the manual final scheme of the National Games,the scheme proposed in this paper has a certain degree of superiority decline,but it can be published in a very short time...