基于粒子群算法的食品加工工艺参数优化模型构建

Construction of optimization model of food processing parameters based on particle swarm optimization

为提高传统食品加工工艺优化效率,缩短优化周期,以泡菜加工为研究对象,构建一个基于粒子群算法的泡菜加工过程优化模型SAE-SVR。首先,对PSO优化算法进行详细介绍;然后结合PSO算法的多目标优化能力,基于Matlab程序完成食品加工工艺参数优化模型构建,实现食品加工工艺参数优化。最后,为验证模型和算法性能是否优越,对算法进行仿真实验。最终结果表明,对PSO算法进行优化处理后,其优化结果与实际值十分接近,两者误差仅为1.2%;且相较于传统的曲面响应优化模型,提出的SAE-SVR模型各个优化参数明显更高。由此可知,PSO优化算法可实现食品加工工艺优化,加工工艺效率显著提升,优化时间减少,模型可视化效果佳,满足设计食品加工工艺优化需求。

In order to improve the optimization efficiency of traditional food processing process and shorten the optimiza-tion period,a SAE-SVR based on pickle processing algorithm is constructed as the research object.Firstly,the PSO optimiza-tion algorithm is introduced in detail;then the multi-objective optimization capability of PSO algorithm completes the food processing parameter optimization model construction based on matlab program to realize the food processing parameter optimi-zation.Finally,to verify whether the model and algorithm performance are superior,simulation experiments on the algorithm.The final result shows that after optimizing the PSO algorithm,the optimization result is very close to the actual value,and the error is only 1.2%.Moreover,the SAE-SVR model is significantly higher.It can be seen that the PSO optimization algorithm can optimize the food processing process,significantly improve the processing process efficiency,reduce the optimization time,and the model visualization effect is good,to meet the needs of the design food processing process optimization.