Research On Application Of Job-shop Scheduling Based On Swarm Intelligence Algorithm

Swarm intelligence algorithm is a heuristic algorithm based on biological group behavior,that is,the complex behavior exhibited by the group is the intelligence highlighted by simple individuals in the interaction process.The swarm intelligence algorithm is simple and easy to implement and has good robustness,so it can be used to solve various complex combinatorial optimization problems.The workshop scheduling problem is the process of rationally allocating the processing tasks of the factory so that the set production goals can be achieved under the condition of satisfying certain constraints.It is considered to be one of the most complex combinatorial optimization problems.Therefore,this paper mainly conducts in-depth research on the particle swarm algorithm in the swarm intelligence algorithm,and applies it to the workshop scheduling problem to study the performance of the algorithm.The main research contents are as follows:A particle swarm optimization algorithm for adaptive parameter change(ACPSO)is proposed on the basis of particle swarm optimization.The inertia weight and learning factor in the algorithm adopt nonlinear adaptive adjustment method.The appropriate adjustment strategy of inertia weight and learning factor can make the algorithm have the development and exploration ability matching the optimization stage,and avoid the premature convergence of the algorithm.From the comparative experimental results of the ACPSO algorithm on the CEC2013 data set,it can be seen that the algorithm shows good performance in the face of function optimization problems,and can obtain better optimization accuracy.In order to further improve the convergence speed of particle swarm optimization and enhance the search ability of the algorithm in the later stage of iteration,an improved tabu particle swarm optimization algorithm TIPSO was proposed.The algorithm combines tabu strategy based on location similarity and particle swarm optimization algorithm,and introduces disturbance factor on this basis.The optimization results on the standard data set show that the algorithm has a good convergence effect,can jump out of the local minima in time,and still has excellent performance in the face of multi-dimensional complex functions.The flexible flow shop scheduling problem is studied,and the goal of this paper is to minimize the maximum completion time to solve the shop scheduling problem.Finally,this paper applies the optimization algorithm to the flexible flow shop scheduling problem,and the practicability and effectiveness of the algorithm are verified by the simulation and comparison experiments with the other two algorithms.