Application And Research Of Improved Differential Evolution Algorithm In Flexible Job Shop Scheduling

In the manufacturing industry,how to improve production efficiency while reducing production costs,maximize equipment utilization,and rationally adjust production resource allocation has always been a problem that companies need to consider in order to create greater profits.The production scheduling problem is one of the core contents of enterprise production management.Flexible Job-Shop Scheduling Problem(FJSP)is a simplified model of many actual production scheduling problems.Because it is closer to the actual production situation,it becomes The current research focus in the field of dispatching at home and abroad.This article aims to study the solution based on the differential evolution algorithm,the main contents are as follows:First,in order to solve the shortcomings of differential evolution(DE)that is easy to fall into local convergence and weaker ability to optimize,adjust its control parameters during the evolution of the algorithm,and reverse the Opposition-Based Learning(OBL)mechanism and differential evolution Algorithm fusion.This algorithm combines OBL to evaluate the current individual and its reverse individual at the initialization stage to generate a reverse population.Then,during the population initialization and to-be-evolved stages,select the optimal population from the two and continue iterating,by searching for more effective regions.Guide individuals to evolve towards the best of the population and enhance the algorithm’s ability to find the best.Secondly,when the evolution of individuals falls into a stagnation stage,Gaussian mutation is introduced with a certain probability,a new method of individual mutation is proposed,and the individual’s evolutionary behavior is corrected in time.Help the individual escape the local best.Improve the development ability of a single individual,balance the global and local optimization ability of the algorithm.Finally,the algorithm has repeatedly tested and compared the standard test functions,showing that the improved algorithm has certain advantages over other optimization algorithms in the test function.At the same time,in order to prove the practical significance of the algorithm,combined with the improved differential evolution algorithm in this paper,the actual production workshop of a machining branch was used as the realistic background to develop the workshop scheduling system,and achieved good practical application results.