黄光球, 孙思雅, 陆秋琴. 基于SEIRS传染病模型的函数优化方法——SEIRS算法[J]. 计算机研究与发展, 2014, 51(12): 2671-2687.
 引用本文: 黄光球, 孙思雅, 陆秋琴. 基于SEIRS传染病模型的函数优化方法——SEIRS算法[J]. 计算机研究与发展, 2014, 51(12): 2671-2687.
Huang Guangqiu, Sun Siya, Lu Qiuqin. SEIRS Epidemic Model-Based Function Optimization Method—SEIRS Algorithm[J]. Journal of Computer Research and Development, 2014, 51(12): 2671-2687.
 Citation: Huang Guangqiu, Sun Siya, Lu Qiuqin. SEIRS Epidemic Model-Based Function Optimization Method—SEIRS Algorithm[J]. Journal of Computer Research and Development, 2014, 51(12): 2671-2687.

## SEIRS Epidemic Model-Based Function Optimization Method—SEIRS Algorithm

• 摘要: 为了解决复杂函数优化问题，采用SEIRS传染病模型提出了SEIRS算法.在该算法中,假设某个生态系统由若干人类个体组成,每个个体均由若干个特征来表征.该生态系统存在一种传染病在个体之间传染，该传染病攻击的是个体的部分特征.每个染病个体均经历易感、潜伏、发病和治愈等阶段，这些阶段的综合作用决定了个体的体质强弱;利用SEIRS传染病模型所描述的疾病传播机理构造出了相关算子,使个体之间能充分交换信息.结果表明：E-E，I-I和R-R算子能使体质强壮的个体向体质弱的个体传递强壮特征信息，使得后者能向好的方向发展；S-E，S-R，E-I(ω)和R-S(ω)算子能使处于不同状态的个体获得其他个体的平均特征信息，从而降低了该个体陷入局部最优解的概率；S-S算子能使个体的活跃度提高，从而扩大其搜索范围；E-R和I-R算子既具有S-S算子的特征又具有S-E，S-R，E-I(ω)和R-S(ω)算子的特征.体质强壮的个体能继续生长，而体质虚弱的个体则停止生长,从而确保本算法具有全局收敛性.测试结果表明：本算法具有搜索能力强的特点，对求解复杂函数优化问题具有很高的收敛速度.

Abstract: To solve some complicated function optimization problems, the SEIRS algorithm is constructed based on the SEIRS epidemic model. The algorithm supposes that some human individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease exists in the ecosystem and infects among individuals; and the disease attacks a part of features of an individual. Each infected individual passes through such stages as suspected, exposed, infected and removed, which determine synthetically the physique strength of an individual. The algorithm uses the transferring mechanism of the infectious disease described by the SEIRS epidemic model to construct some operators so as to enable individuals to exchange feature information among them easily. Results show that the E-E, I-I and R-R operator can transfer feature information from some strong individuals to a weak individual so as to make the latter grow better; the S-E, S-R, E-I(ω) and R-S(ω) operator ensure an individual to obtain average feature information from other individuals so as to reduce probability that the individual drops into local optima; the S-S operator can expand an individual’s search scope by increasing its vitality; the E-R and I-R operator have the characteristics of both the S-S operator and the S-E, S-R, E-I(ω) and R-S(ω) operator; The individuals with strong physique can continue to grow, while the individuals with weak physique stop growing, which ensures the algorithm to have global convergence. Some case studies show that the algorithm has characteristics of strong search capability and high convergence speed for the complicated functions optimization problems.

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