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

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