Analysis And Research Of Multi-objective Flexible Job Shop Scheduling Based On Improved ICA

In recent years,the requirement of the manufacturing system,changing customer demand oriented,diversified products,gradually became the main mode of production,mass production in the enterprise,in the process of actual production scheduling is the key to efficiency,flexible job shop scheduling from the traditional job shop scheduling problem extension,joined the constraint condition and multi-objective optimization,complete different business needs,When choosing the research method of the problem,it is necessary to combine different objectives to determine the appropriate algorithm and establish the scheduling model,which has important theoretical and practical significance to solve the demand of actual production.In this context,this paper focuses on the study of multi-objective flexible job shop scheduling.By improving the combination of standard Empire competition algorithm and Pareto optimal idea,local search optimization in multi-objective can improve the quality of solutions.The specific research contents are as follows:(1)In view of the empire competition algorithm in the process of slow convergence speed and fall into the local optimal problem,set the initial parameters,add reform mechanism and gaussian and cauchy mutation operator and improve the convergence speed,jump out of local optimum,the swarm optimization algorithm simulation results on benchmark functions,the application feasibility of the improved algorithm is discussed.(2)According to single objective flexible job shop scheduling problem,scheduling code conversion activity scheduling,and dispatching gantt chart by time matrix directly by introducing variable selection and mutation in the mutation search increase the diversity of colony,mutation couldn’t be more optimal scheduling choice crossover operation increase the diversity of colony moves,the validity of the algorithm is tested in classical FT and LA scheduling problems and the optimal solution matrix is given.(3)For the multi-objective flexible job shop scheduling problem,the key processes are determined on the basis of the disjunctive graph model.In order to achieve Pareto optimization,a frontier transformation mechanism is proposed to add high-quality solutions into the non-dominated solution set.Based on the multi-objective design of three local search methods,HSICA is proposed to solve the multiobjective FJSP.Simulation results show that the proposed algorithm is feasible and efficient.The experimental results of MATLAB show that the times of finding the optimal solution of the proposed algorithm is better than other intelligent optimization algorithms in classical scheduling problems.The non-dominated solution set of HSICA is superior to other algorithms and has better performance on Kcaem and BRdata data sets.