基于共生系统的人工鱼群算法在饲料配方优化中的应用

doi:10.11772/j.issn.1001-9081.2016.12.3303

计算机应用 ›› 2016, Vol. 36 ›› Issue (12): 3303-3310.DOI: 10.11772/j.issn.1001-9081.2016.12.3303

基于共生系统的人工鱼群算法在饲料配方优化中的应用

刘庆^1,2, 李迎³, 庆麦玉⁴, 小高知宏⁵

1. 西安理工大学自动化与信息工程学院, 西安 710048;
2. 陕西省复杂系统控制与智能信息处理重点实验室, 西安 710048;
3. 西安理工大学计算机科学与工程学院, 西安 710048;
4. 陕西省畜牧技术推广总站, 西安 710016;
5. 福井大学工学研究科, 日本福井 910-8507

收稿日期:2016-06-08 修回日期:2016-06-29 出版日期:2016-12-10 发布日期:2016-12-08
通讯作者: 刘庆
作者简介:刘庆(1983-),男,陕西西安人,讲师,博士,主要研究方向:进化计算、数据驱动建模;李迎(1987-),女,陕西西安人,博士研究生,主要研究方向:进化计算;庆麦玉(1956-),女,陕西渭南人,教授,主要研究方向:动物营养、畜场管理;小高知宏(1963-),男,日本人,教授,博士,主要研究方向:生物信息处理、智能人机接口、网络安全。
基金资助:
国家自然科学基金资助项目（61502385）。

Application of symbiotic system-based artificial fish school algorithm in feed formulation optimization

LIU Qing^1,2, LI Ying³, QING Maiyu⁴, ODAKA Tomohiro⁵

1. School of Automation and Information Engineering, Xi'an University of Technology, Xi'an Shaanxi 710048, China;
2. Shaanxi Key Laboratory for Complex System Control and Intelligent Information Processing, Xi'an Shaanxi 710048, China;
3. School of Computer Science and Engineering, Xi'an University of Technology, Xi'an Shaanxi 710048, China;
4. Animal Husbandry Technical Extension General Station of Shaanxi Province, Xi'an Shaanxi 710016, China;
5. Graduate School of Engineering, University of Fukui, Fukui-ken 910-8507, Japan

Received:2016-06-08 Revised:2016-06-29 Online:2016-12-10 Published:2016-12-08
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61502385).

摘要/Abstract

摘要： 考虑到智能算法对各类饲料配方优化模型的广泛适用性，首次将人工鱼群算法（AFSA）应用于饲料配方优化。为满足饲料配方优化对收敛精度的要求，采用了一种基于共生系统的人工鱼群算法运行框架，显著提高了原算法的收敛精度与速度。在优化过程中，人工鱼在解空间的位置直接以饲料配比进行编码，采取基于罚函数的评价函数计算其适应度；人工鱼以预定的行为策略执行各行为算子对解空间进行搜索。最后三个实际算例验证了所提算法的有效性。验证结果表明，所提算法设计出的饲料配比方案的吨成本显著降低，各项营养达标，提出算法的优化性能明显优于其他已有算法。

关键词: 共生系统, 饲料配方, 人工鱼群算法, 优化

Abstract: In consideration of intelligence algorithms' extensive applicability to various types of feed formulation optimization models, the Artificial Fish Swarm Algorithm (AFSA) was firstly applied in feed formulation optimization. For meeting the required precision of feed formulation optimization, a symbiotic system-based AFSA was employed. which significantly improved the convergence accuracy and speed compared with the original AFSA. In the process of optimization, the positions of Artificial Fish (AF) individuals in solution space were directly coded as the form of solution vector to the problem via the feed ratio, a penalty-based objective function was employed to evaluate AF individuals' fitness. AF individuals performed several behavior operators to explore the solution space according to a predefined behavioral strategy. The validity of the proposed algorithm was verified on three practical instances. The verification results show that, the proposed algorithm has worked out the optimal feed formulation, which can not only remarkably reduce the fodder cost, but also satisfy various nutrition constraints. The optimal performance of the proposed algorithm is superior to the other existing algorithms.

Key words: symbiotic system, feed formulation, Artificial Fish School Algorithm (AFSA), optimization

中图分类号:

刘庆, 李迎, 庆麦玉, 小高知宏. 基于共生系统的人工鱼群算法在饲料配方优化中的应用[J]. 计算机应用, 2016, 36(12): 3303-3310.

LIU Qing, LI Ying, QING Maiyu, ODAKA Tomohiro. Application of symbiotic system-based artificial fish school algorithm in feed formulation optimization[J]. Journal of Computer Applications, 2016, 36(12): 3303-3310.

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基于共生系统的人工鱼群算法在饲料配方优化中的应用

Application of symbiotic system-based artificial fish school algorithm in feed formulation optimization

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