Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (12): 3303-3310.DOI: 10.11772/j.issn.1001-9081.2016.12.3303

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

LIU Qing1,2, LI Ying3, QING Maiyu4, ODAKA Tomohiro5   

  1. 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).


刘庆1,2, 李迎3, 庆麦玉4, 小高知宏5   

  1. 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
  • 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:

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

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

CLC Number: