Research On The Scheduling Problem Based On Improved Partical Swarm Optimization

The course arrangement management of the intelligent laboratory aims at the course arrangement of the laboratory and realizes the teaching task arranged uniformly in the school educational administration office.Its content is to make a reasonable and feasible schedule according to the teaching plan,which is the core function of the whole laboratory teaching and laboratory management.The core factors involved in the course arrangement include time,students,teachers,courses and classrooms.This is a non-linear multi-objective combination optimization problem,which generally does not exist,the only optimal solution.Therefore,the key idea to deal with the course arrangement is to determine the near optimal solution,which can significantly reduce the calculation difficulty.Aiming at the realization of the detailed task of course arrangement in the laboratory,this paper firstly introduces the corresponding achievements of foreign and domestic researchers in the field of course arrangement in Colleges and universities,and proposes a new algorithm based on particle swarm optimization to solve a large number of complex and non-linear problems of course arrangement by combining induction and analysis with relevant theories.In this paper,the improved particle swarm optimization algorithm is used to solve the scheduling problem,which provides a solution for the actual scheduling management.Problem solving is the problem of arranging courses.It also has some practical value in teaching resources,not only for experimental resources,so it can better deal with the arrangement of general courses of educational administration.In this paper,the relative reasonable particle swarm optimization parameters are determined through experiments,which can enhance the efficiency of the algorithm,thus greatly reducing the time required for the actual course arrangement.The research direction of this paper mainly includes:1.The main sub population is generated by splitting the particle population,and the sub population is dealt with iteratively to determine the optimal solution among the sub groups,and then the parent group and the optimal solution among the sub groups are dealt with iteratively.The in-process control is adopted,that is,the system maturity is determined in the iterative process,and the immature system is mutated to avoid the difficulty of the PSO into the optimal solution.2.Establish the hard condition constraint model of experimental resource allocation and the general fitness function of multi-directional,design the update speed and position of particles,and design the termination conditions of the algorithm.3.Using the experimental course of the second semester of 2017-2018 in the school of chemical engineering of Donghua University as an example to verify,the fitness function values with different population size and iteration times are compared,so as to determine the optimal solution of setting parameters.By comparing with other algorithms and the corresponding solutions of PSO,it is found that PSO can better deal with a large number of complex and nonlinear scheduling problems.