Application Of Cooperative Multi-population Optimization Algorithm In Flexible Job Shop Scheduling

With the development of economy and the continuous expansion of market,the production scale of enterprises must be constantly expanded,and in order to meet the needs of consumers,the production complexity of products is also increasing.Therefore,the scheduling problem arises.As a key issue in the production chain,a reasonable scheduling scheme can make production more efficient,achieve cost savings and enhance market competitiveness.The flexible job shop scheduling problem exists widely in practical applications.Because of its high computational complexity,it is difficult to obtain satisfactory scheduling schemes.Therefore,many researchers are attracted to study the problem and try to find more effective ways to solve the flexible job shop scheduling problem,which also makes the problem become a research hotspots in recent years.Because of the high complexity of the flexible job shop scheduling problem,it is difficult for traditional planning methods to obtain satisfactory scheduling schemes.Therefore,researchers use evolutionary computation based on heuristics to solve flexible job shop scheduling problem.As a branch of evolutionary computing,the swarm intelligence algorithm obtains intelligence through social learning,and its optimization process does not depend on the mathematical model.Therefore,the swarm intelligence algorithm has attracted wide attention,and provides a new research idea and direction for solving complex flexible job shop scheduling problems.In this paper,the particle swarm optimization is used to solve flexible job shop scheduling problems.The main research contents of this paper are as follows:This paper analyses the current situation of flexible job shop scheduling problems,determines objective functions according to the characteristics and evaluation indexes of flexible job shop scheduling problems,and constructs the mathematical model of the multi-objective flexible job shop scheduling problem.A multi-objective particle swarm optimization algorithm based on cooperative hybrid strategy is proposed to solve complex flexible job shop scheduling problems.This method combines multiple strategies,uses multi-population strategy to improve global search ability of the algorithm,and uses dynamic clustering strategy to enhance the population diversity and reduce the probability of the algorithm falling into local optimum,The proposed algorithm is used to solve complex flexible job shop scheduling problems.The results show that this algorithm has good optimization performance and can obtain a reasonable scheduling scheme.To effectively balance the relationship between objectives in flexible job shop scheduling problems,a multi-objective particle swarm optimization algorithm based on local cooperative multi-population is proposed.This algorithm uses local cooperative strategy to divide the population into several sub-populations.Sub-populations select leading particles according to the non-dominated sorting and differential mutation,and balance all objectives while expanding the search range of the population.In this paper,the proposed algorithm is used to solve complex flexible job shop scheduling problems.The results show that this algorithm can effectively solve complex flexible job shop scheduling problems.