•  
  •  
 

Abstract

In order to improve the quality and efficiency of coffee blend, a method based on interactive genetic algorithm (IGA) for it was studied. The blending solutions were used as individuals, with the improved adaptive crossover and mutation probability, and the interactive interface provides 10 indexes for individual fitness, and then five users participated the optimization. The results showed that the method was easy to operate, a consistent satisfactory solution could be obtained within 4~5 generations, with the difference of component ratio less than 5% and the fitness more than 85. Compared with the traditional cup testing, the results showed that the IGA optimization method was superior to the traditional one. Based on the successful application in evolutionary optimization of IGA, this research provided a new intelligent way for coffee blends.

Publication Date

3-28-2017

First Page

195

Last Page

198

DOI

10.13652/j.issn.1003-5788.2017.03.041

References

[1] 伊记. 咖啡鉴赏[M]. 北京: 新世界出版社, 2014: 195-203.
[2] 郭光玲. 咖啡师手册[M]. 北京: 化学工业出版社, 2008: 29-34.
[3] 张粤华. 咖啡调制[M]. 重庆: 重庆大学出版社, 2013: 2-9.
[4] 李冬冬, 贾柳君, 张海红, 等. 基于介电谱技术结合遗传算法的草莓品质预测[J]. 食品工业科技, 2016, 37(11): 141-146.
[5] 谈亚丽, 李啸, 邹嫚, 等. 基于BP神经网络和遗传算法的面包酵母高密度发酵培养基优化[J].工业微生物, 2013, 43(1): 64-68.
[6] 朱晓媛, 胡铁, 黎继烈, 等. 基于遗传算法的纤维素酶分批发酵动力学研究[J]. 中国食品学报, 2014, 14(1): 47-51.
[7] 沙如意, 楼坚, 蔡成岗, 等. 基于遗传算法优化BP神经网络的丙酮丁醇梭菌发酵预测模型研究[J]. 发酵科技通讯, 2016, 45(1): 23-26.
[8] 徐利, 周丽伟, 郭文强, 等. 基于支持向量机-遗传算法灰树花发酵模型的建立及优化[J]. 食品科学, 2016, 37(11): 143-146.
[9] 张良安, 马寅东, 单家正, 等. 基于遗传算法和Petri网络的机器人装配生产线平衡方法[J]. 食品与机械, 2012, 28(2): 79-82.
[10] 王小勇, 李兵, 曾晨, 等. 基于遗传算法的茶叶理条机参数优化设计[J]. 茶叶科学, 2016, 36(4): 440-444.
[11] 张国平. 食品机械平面四杆机构的遗传算法优化设计[J]. 食品与机械, 2010, 26(3): 117-119.
[12] 崛口俊英. 咖啡完全掌握手册[M]. 福州: 福建科学技术出版社, 2014: 62-76.
[13] GONG Dun-wei, CHEN Jian, SUN Xiao-yan, et al. Evaluating individuals in interactive genetic algorithms using variational granularity[J]. Soft Computing, 2015, 19(3): 621-635.
[14] GONG Dun-wei, JI Xin-fang, SUN Jing, et al. Interactive evolutionary algorithms with decision-maker's preferences for solving interval multi-objective optimization problems[J]. Neurocomputing, 2014, 137(4): 241-251.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.