系统仿真学报 ›› 2020, Vol. 32 ›› Issue (5): 767-781.doi: 10.16182/j.issn1004731x.joss.19-0077

• 专栏:公共卫生安全 • 上一篇    下一篇

人感禽流感优化算法

黄光球, 陆秋琴   

  1. 西安建筑科技大学管理学院,陕西 西安 710055
  • 收稿日期:2019-02-28 修回日期:2020-03-25 出版日期:2020-05-18 发布日期:2020-05-15
  • 作者简介:黄光球(1964-),男,湖南桃源,博士,教授,研究方向为计算机仿真、计算智能。
  • 基金资助:
    国家自然科学基金(71874134),陕西省自然科学基础研究计划-重点项目(2019JZ-30),陕西省社会科学基金(2018S49,2017S035)

Optimization Algorithm Based on Human Infection with Avian Influenza

Huang Guangqiu, Lu Qiuqin   

  1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China
  • Received:2019-02-28 Revised:2020-03-25 Online:2020-05-18 Published:2020-05-15

摘要: 为求解一类复杂非线性优化问题的全局最优解,采用跨物种传播的人感禽流感传染病动力学模型提出了人感禽流感传染病优化算法。利用H7N9传染病模型构造出的Su-Su,Iu-Iu,Su-Iu,Iu-Su,Su-Du,Iu-Du等算子能使个体能在同物种和跨物种个体之间充分交换信息,其中Su-Su、Iu-Iu算子可利用强壮个体的特征来改善虚弱个体的特征,从而提升算法的求精能力;Su-Iu、Iu-Su算子可改良个体的适应度分布特征,从而提升算法的探索能力;Su-Du,Iu-Du算子可使极虚弱个体得到有效清除,从而降低算法陷入局部陷阱的概率。测试案例表明:本算法可快速求解一类维数较高的复杂非线性优化问题。

关键词: 群智能优化算法, 仓室模型, 传染病动力学

Abstract: To get the global optimal solution of some complex nonlinear optimization problems, an optimization algorithm based on the human avian influenza infectious diseases is proposed by using its dynamic model of cross species transmission. Applies the H7N9 infectious disease model to create the operators Su-Su, Iu-Iu, Su-Iu, Iu-Su, Su-Du, Iu-Du, and to enable the individuals to exchange information among the same species and cross-species. The Su-Su and Iu-Iu operators can improve the characteristics of the weak individuals by that of the strong individuals, thus the exploitation ability of the algorithm can be improved. The Su-Iu and Iu-Su operators can improve the fitness distribution characteristics of the individuals, therefore, the exploration ability of the algorithm can be improved. The Su-Du and Iu-Du operators can effectively remove the extremely weak individual, thereby the probability that the algorithm falls into a local trap can be reduced. The test cases show that the algorithm can quickly solve some complex nonlinear optimization problems with high dimensions.

Key words: swarm intelligent optimization algorithm, bin model, epidemic dynamics

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