Hybrid Improved Sparrow Search Algorithm For Job-shop Scheduling Problem

With the rapid development of China’s manufacturing industry,people’s demand for products is getting higher and higher.The main performance is to accelerate the upgrading of products,a wide range of consumer product demand,high product quality level,fast production speed.Consumer demand for products increasingly personalized and diversified development.In order to improve productivity and competitiveness,enterprises must rationally allocate production resources and improve production efficiency.Shop floor scheduling plays a decisive role in determining production efficiency.Sparrow search algorithm as a new algorithm,although the proposed time is short,but because of its algorithm parameters are less,simple structure,global search and local search ability has been successfully applied to various practical applications.More and more researchers are interested in job-shop scheduling optimization.At present,sparrows algorithm is seldom used in discrete job shop scheduling.In this paper,based on the optimization process of the sparrow search algorithm and the specific situation of job shop scheduling problem,the shortcomings of the sparrow search algorithm are analyzed,a hybrid improved sparrows search algorithm(HISSA)is proposed to optimize the maximum makespan of single-objective job shop scheduling problem by using the advantages of genetic algorithm and simulated annealing algorithm.This paper analyzes the advantages and disadvantages of the sparrow search algorithm,finds out the defects in the search process,and improves it effectively.First of all,the original standard sparrow search algorithm uses random initialization,which produces a poor initial solution quality,uneven distribution affects the later iteration optimization.In order to solve this problem,the initial population quality of sparrows was improved by introducing Tent chaotic MAP INSTEAD OF RANDOM INITIALIZATION Through the analysis of the position update formulas of the discoverer and the follower,it can be seen that the diversity of the population decreases after the position update of the discoverer and the follower,and the searching process of the Algorithm is easy to fall into the problem of local optimum.In order to solve these problems,the original position of discoverer and follower is updated,and then the individuals are evenly cross-operated by genetic algorithm to increase the diversity of Sparrow Population In order to enhance the local search ability of the vigilantes,a reverse mutation operation is carried out on the new individuals after the position of the vigilantes is updated,and a simulated annealing algorithm is introduced after the Sparrow Search Algorithm,the simulated annealing mechanism is used to help the sparrows search algorithm jump out of local extremum to improve its global search ability.In the experimental part,the HISSA performance is verified by 10 classical examples of job shop scheduling problem,and then two scheduling examples are simulated.Compared with other algorithms,HISSA is proved to be effective in solving actual job shop scheduling problem,the results show that HISSA can not only solve the job-shop scheduling problem effectively,but also has better optimization effect and robustness than the contrast algorithm.