摘要
针对番茄酱产季的番茄原料供应不均衡问题,提出一种多目标粒子群生物地理学算法对番茄规划模型进行求解。结合Pareto快速非支配排序法和粒子群与生物地理学算法的操作机制,能够更好地增加算法的全局搜索性能。构建以使企业经济损失最小、种植户总净收益最大和使种植面积最小为番茄规划的三个目标函数。以某番茄酱厂提供的环境参数为例,MATLAB仿真计算结果表明,在求解番茄种植规划问题上,该算法比传统的进化算法表现更好,能够获得合理的种植规划方案。
Aimed at the imbalance supply of the materials during tomato sauce season,a multi-objective biogeography-based optimization with particle swarm optimization(PSOBBO)is proposed to solve the tomato programming model.Pareto fast non-dominant sorting method and PSOBBO were combined,which could better increase the global search performance of the algorithm.Three objective functions of tomato plant planning were constructed to minimize the economic loss of enterprises,maximize the total net income of farmers and minimize the planting area.Taking the environmental parameters provided by a tomato ketchup factory as an example,the MATLAB simulation results show that this algorithm performs better than the traditional evolutionary algorithm in solving the tomato planting planning problem,and can obtain the reasonable planting planning scheme.
作者
罗丹
蒋兵兵
Luo Dan;Jiang Bingbing(School of Intelligent Control of Rail Transit,Hunan Railway Professional Technology College,Zhuzhou 412000,Hunan,China;College of Railway Power Supply and Electrical Engineering,Hunan Vocational College of Railway Technology,Zhuzhou 412000,Hunan,China)
出处
《计算机应用与软件》
北大核心
2023年第7期294-299,共6页
Computer Applications and Software
基金
湖南省教育厅科学研究项目(18C1524)
湖南省自然科学基金项目(2021JJ60068)。
关键词
生物地理学算法
粒子群
均衡供应
多目标优化
Biogeography-based optimization
Particle swarm optimization
Balanced supply
Multi-objective optimization
