Abstract: To solve some complicated optimization problems, this paper constructs the SIR-DNA algorithm by using the DNA-based SIR (susceptible-infectious-recovered) epidemic model. The algorithm takes the solving process of an optimization problem as the process that an infectious disease spreads among animal individuals in an ecosystem, and its spreading law can be described by the SIR epidemic model. What the infectious disease attack is some sites lying in some pathogenic genes of an animal individual. For different animal individual, which pathogenic genes and associated sites are attacked by the contagious disease, are completely random. If an infected individual is cured, which immune genes and associated sites will possess of immunization, are completely random. Because an infectious disease attacks only a small part of genes each time, only a small number of variables will be processed each time, thus a natural dimension reduction is realized. Because the DNA-based SIR epidemic model is applied, the difference of pathogenic mechanisms among different infectious diseases can be differentiated. The algorithm  uses the transferring mechanism of the infectious disease described by the SIR epidemic model to construct S-S, S-I, I-I, I-R, R-R and R-S operators so as to enable individuals to exchange feature information among them easily, fully and naturally. Some case studies show that the algorithm has the characteristics of strong search capability and high convergence speed for the complicated functions optimization problems.

Key words: evolution algorithm, population-based intelligent optimization algorithm, meta-heuristic search, epidemic dynamics, SIR epidemic model

摘要： 为了解决某些复杂优化问题，采用基于DNA的SIR（susceptible-infectious-recovered）传染病模型构建了SIR-DNA算法。该算法将优化问题的求解过程设想成一种传染病在一个生态系统中的若干动物个体之间传播的过程，其传播规律可用SIR传染病模型描述。传染病攻击的是动物个体若干个致病基因中某些位点。对于不同的动物个体，其哪些致病基因及其中的哪些点位被攻击完全是随机的；若一个动物个体被治愈，其哪些免疫基因及其中的哪些位点获得免疫也完全是随机的。因传染病每次攻击的是个体的极小部分基因，故每次处理的变量数很少，从而实现了算法的天然降维；因采用基于DNA的SIR传染病模型，故可以区分不同的传染病毒在致病机理之间的差异；利用SIR模型所描述的疾病传播机理构造出了S-S、S-I、I-I、I-R、R-R和R-S等算子，使个体之间能通过疾病传播天然地充分交换信息。测试结果表明，该算法具有搜索能力强的特点，对求解复杂优化问题具有很高的收敛速度。

关键词: 进化算法, 群智能优化算法, 元启发式搜索, 传染病动力学, SIR传染病模型