Journal of Frontiers of Computer Science and Technology ›› 2019, Vol. 13 ›› Issue (11): 1965-1980.DOI: 10.3778/j.issn.1673-9418.1810040

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Plague Infectious Disease Optimization Algorithm

HUANG Guangqiu, LU Qiuqin   

  1. School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China
  • Online:2019-11-01 Published:2019-11-07



  1. 西安建筑科技大学 管理学院,西安 710055

Abstract: In order to solve some optimization problems with many local optimal solutions, a new swarm intelligence algorithm, the plague infectious disease optimization (PIDO) algorithm, is proposed by using the dynamic model of plague infectious disease with pulse vaccination and time delay. In the algorithm, it is assumed that there are several villagers living in a village and every villager is characterized by some features. The plague virus is prevalent in the village and the villagers are infected with the disease through effective contact with sick rats. The plague virus attacks a very small part of features of human body. Under the action of the plague virus, the growth state of each villager will be randomly transformed among 4 states of susceptibility, exposure, morbidity and recovery, thus realizing the random search for the global optimal solution. The degree of physical strength of a villager is described by the HHI index. The higher the HHI index of a villager is, the stronger its physical strength will be, also the higher the possibility of its growth will be. The PIDO algorithm has 9 operators such as S_S, S_E, E_E, E_I, E_R, I_I, I_R, R_R, R_S, and each operator only deals with the 1/1000~1/100 of the total number of variables each time. Results of case study show that the PIDO algorithm has the characteristics of fast search speed and global convergence, and is suitable for solving the global optimization problem with higher dimensions.

Key words: swarm intelligence optimization algorithm, global optimization, plague transmission dynamic model

摘要: 为了求解多局部最优解的优化问题,采用具有脉冲预防接种的时滞鼠疫传染病动力学模型,提出了一种新的群智能算法——鼠疫传染病优化算法(PIDO)。在该算法中,假设某个村庄生活有若干村民,每个村民均由一些特征来表征;鼠疫病毒在该村庄流行,村民通过与病鼠有效接触而染上该传染病;鼠疫病毒攻击的是人体的很少部分特征,在鼠疫病毒作用下,每个村民的生长状态会在易感、暴露、发病、治愈这4个状态之间随机转换,从而实现对全局最优解的随机搜索;村民的体质强弱程度用HHI指数描述,村民的HHI指数越高,其体质越强,继续生存的可能性也越高。PIDO算法拥有S_S、S_E、E_E、E_I、E_R、I_I、I_R、R_R、R_S等9个算子,演化时每个算子每次仅处理总变量数的1/1 000~1/100。案例研究结果表明,PIDO算法具有搜索速度快和全局收敛性的特点,适于求解维数较高的全局优化问题。

关键词: 群智能优化算法, 全局优化, 鼠疫传播动力学模型