Research Of The Allocation Of Task In Picking Fruit Corn Based On Genetic Algorithm And Particle Swarm Optimization

China is a large agricultural country, agriculture has been the focus of national attention in the developing industry. Agricultural information refers to the modern information technology and information systems applying to agriculture. Central file has been emphasized the problem of agricultural information four years in a row. The agriculture can transform from the past low-tech, low-value-added industries into high-tech, high value-agriculture by agricultural information. Health food is the topic which the people take an interest.In recent years,the corn with thin-skinned,juicy, sweet and fragrant, nutritious has been into the public eye,and has been very popular in the market.Dai Town, Xinzhou City, Shanxi Province in2010has been cultivation of fruit corn in a large-scale.During harvest time in August, due to the division of labor unreasonably, a lot time has been wasted, which leading to the market efficiency of fruit corn has been affected. In fact, this problem which involves is a combination of optimization,seeking for the optimal solution. That is how to make the fruit corn complete in the shortest possible time of harvest in a limited labor, In order to improve work efficiency, and to gain the most.My research is to study the allocation of task in picking fruit corn based on genetic algorithm and particle swarm optimization, which has important theoretical significance and value in use about agricultural popularization and application of information technology.In this thesis,applying the genetic algorithm and particle swarm optimization to the specific labor tasks assigned of the picking team when fruit corn harvested,then mathematical modeling,finally, moving on to the simulation in MATLAB.By comparing with the experimental results, the preliminary results are that particle swarm optimization for solving this problem has the ad vantage.than genetic algorithm. Then doing experiments in different scale using these two algorithms,the experimental results fully explain the PSO algorithm is more suitable than GA to solve this kind of problem,and reach the optimal allocation scheme.