摘要
为提高动物饲料配方的营养,降低饲料成本,在保证饲料营养的前提下,构建最小成本的目标函数,并采用改进遗传算法对目标函数的最优配方进行求解。仿真实验表明,以20~50 kg的生猪为实验范例,分别采用标准遗传算法和改进遗传算法求解100 kg生猪饲料配方,并将两种方法求解得到的最终饲料配方进行对比。实验结果表明:通过标准遗传算法求解获得的100 kg生猪饲料配方的总成本为307.91元,而改进的遗传算法求解获取的100 kg生猪饲料配方的总成本为291.03元,比传统遗传算法获取的配方减少了16.88元;标准遗传算法在经过650次迭代后陷入了局部最优解,而改进遗传算法在经过700次迭代后变化曲线趋于平稳,输出全局最优解291.03元。由此得出,改进遗传算法的全局寻优能力更强,可快速、有效求解动物饲料最优配方。
To improve the nutrition of animal feed formulas and reduce feed costs,a minimum cost objective function is constructed while ensuring feed nutrition,and an improved genetic algorithm is used to solve the optimal formula of the objective function.The simulation experiment shows that using 20-50 kg live pigs as experimental examples,the standard genetic algorithm and improved genetic algorithm are used to solve the 100 kg pig feed formula,and the final feed formula obtained by the two methods is compared.The experimental results show that the total cost of the 100 kg pig feed formula obtained through standard genetic algorithm is 307.91 yuan,while the total cost of the 100 kg pig feed formula obtained through improved genetic algorithm is 291.03 yuan,which is 16.88 yuan less than the formula obtained through traditional genetic algorithm;The standard genetic algorithm fell into a local optimal solution after 650 iterations,while the improved genetic algorithm stabilized its change curve after 700 iterations,outputting a global optimal solution of 291.03 yuan.From this,it can be concluded that the improved genetic algorithm has stronger global optimization ability and can quickly and effectively solve the optimal formula for animal feed.
作者
龙艳
LONG Yan(Xinjiang Applied Vocational and Technical College,Kuitun 833200,China)
出处
《粮食与饲料工业》
CAS
2024年第3期47-51,共5页
Cereal & Feed Industry
关键词
机器学习
遗传算法
饲料配方
饲料成本
machine learning
genetic algorithms
feed formulation
feed cost
