Research On Multi-objective Grasshopper Algorithm For Agricultural Product Scheduling Optimization

With the progress of science and technology,the production technology has been greatly developed,and the mode of production has also changed.For the production industry,how to keep up with the pace of the times and meet the growing demand of people for commodities has become the focus of research of various enterprises,especially the agricultural products production and processing enterprises,In order to face the growing number of orders and the small batch,multi batch order mode,it is urgent to find a suitable workshop production and processing mode to replace the traditional production and processing mode,in order to quickly and effectively develop the agricultural product workshop scheduling plan.Due to a large number of uncertain factors in the workshop and the small batch and multi batch order mode,it greatly improves the difficulty of making the scheduling scheme.In addition,each enterprise has its own different requirements and Optimization for different objectives,such as the optimization of production time,the optimization of energy consumption and the optimization of equipment utilization.Therefore,in view of the above problems,this paper will rely on the provincial major special project "research and development and application of big data cloud platform for quality and safety monitoring and traceability of characteristic forest and fruit products",and carry out in-depth research on the dry fruit processing workshop of XX company.1)In this thesis,we use multi-objective optimization to optimize the job shop scheduling,so we focus on the multi-objective grasshopper optimization algorithm based on co evolution.This paper proposes a multi-objective grasshopper optimization algorithm based on multi population co evolution framework,which can achieve a good balance between exploration and development.In order to improve the convergence and diversity of multi-objective optimization solutions,and to explore and develop the balanced swarm intelligence algorithm,a grouping mechanism and co evolution mechanism are designed and integrated into the framework.The grouping mechanism is used to improve the diversity of search operators and the coverage of search space.Through the interaction between search populations,the co evolution mechanism is used to improve the convergence speed of the algorithm.Then,several standard test functions,such as cec2009,ZDT and DTLZ,are used to benchmark the proposed algorithm.The convergence and diversity of the obtained multi-objective optimization solution and the original multi-objective grasshopper algorithm are quantitatively and qualitatively compared by using IGD and GD performance indicators.The results show that the diversity and convergence of the obtained solution are significantly improved,Finally,Wilcoxon rank sum test is used to verify the validity of the results.2)According to the on-the-spot investigation and information analysis of the dry fruit processing workshop of XX company,the constraint conditions and objective function are numerically processed,so as to establish a multi-objective flexible workshop model which is consistent with the actual situation.At the same time,the multi-objective grasshopper optimization algorithm based on co evolution is used to solve and optimize the model,and finally the production scheduling optimization scheme is obtained.Finally,the production scheduling optimization system of dry fruit agricultural products processing workshop is designed and implemented.