基于改进粒子群算法的农产品召回优化  

Optimization of agricultural products recall based on modified particle swarm algorithm

作　　者：李锋[1] 吴华瑞[1] 朱华吉[1] 朱丽[1] 李飞飞[1] 

机构地区：[1]国家农业信息化工程技术研究中心,北京100097

出　　处：《农业工程学报》2013年第7期238-245,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(60871042;61102126);国家科技支撑计划项目(2011BAD21B02);核高基重大专项(2010ZX01045-001-004);北京市农林科学院博士后基金项目;北京市农林科学院院青年科研基金项目

摘　　要：农产品加工是食品供应链质量控制的关键环节。该文针对农产品加工环节的产品召回优化问题,给出了批次分散优化模型并分析了其算法复杂度。针对优化模型为NP难度(non-deterministic polynomial hard),难以求解的问题,指出了采用粒子群优化进行求解的途径。针对粒子群优化算法在进化的初期收敛速度快,易引起早熟;在进化后期收敛速度慢,易引起振荡的问题,提出了一种基于分段门限粒子替换策略的改进粒子群优化算法。采用相关算例对该文提出的改进粒子群优化算法进行优化性能验证,并与类似智能优化算法进行性能对比。算例仿真和性能对比的结果表明,该算法运算开销约为同类算法的10%,且可以降低潜在的召回规模约30%,适用于农产品加工环节的产品召回优化。Agricultural products processing is the key node in food supply chain quality control and products recall is the last shelter to protect the safety of consumers. The potential recall can be reduced by optimization of batch dispersion in the production planning stage of agricultural product processing based on satisfying the processing process and the processing technology. In this paper, the recall optimization in agricultural products processing node was researched. For the recall optimization in agricultural products processing, a four-level batch dispersion model was given by reference the food batch dispersion model. The model consists of four levels, respectively raw materials, components, semi-finished products and finished products. The model has there operations, respectively disassembling, assembling and packaging. In each level, composed several batches belong to different types and batches have ID, type and size three properties. The optimization model of four-level model is given, and its computational complexity was analyzed. According to the batch dispersion model is a NP hard problem, difficulty to resolve, particle swarm optimization was referred to solve it. In the early stage of PSO evolution, the fast convergence can easily cause premature, but in the later stage, slow convergence can easily cause oscillations. A modified PSO based on piecewise threshold particle replacement strategy was proposed to solve this problem. The formal description and diagrammatic of particle replacement strategy based on piecewise threshold were given and the setting of the threshold was discussed. Because the optimization model has more constraints and most of them are linear constraints, the penalty functions were introduced to solve the linear equality and inequality constraints in the recall optimization model. Relevant example was used to verify MPSO, and performance was compared with similar intelligent optimization algorithms. Numerical simulation and performance comparison show that the algorithm is efficien

关 键 词：质量控制 优化 算法 农产品 召回 批次 粒子群优化 

分 类 号：S21[农业科学—农业机械化工程]