Coronavirus herd immunity optimizer (CHIO)
Coronavirus herd immunity optimizer (CHIO)
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DOI：
10.1007/s00521-020-05296-6
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发表时间：
2020-08-27
影响因子：
6
通讯作者：
Abu Doush, Iyad
中科院分区：
文献类型：
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作者：
Al-Betar, Mohammed Azmi;Alyasseri, Zaid Abdi Alkareem;Abu Doush, Iyad
本文提出了一种新的自然启发的基于人的优化算法，称为冠状病毒群体免疫优化器（CHIO）。CHIO的灵感源自群体免疫概念，作为应对冠状病毒大流行（COVID-19）的一种方法。冠状病毒感染的传播速度取决于感染者如何与其他社会成员直接接触。为了保护社会其他成员免受疾病的影响，健康专家建议保持社交距离。群体免疫是当大多数群体免疫时群体达到的状态，其结果是预防疾病传播。这些概念是根据优化概念建模的。CHIO模仿群体免疫策略以及社交距离概念。三种类型的个体病例用于群体免疫：易感、感染和免疫。这是为了确定新生成的解决方案如何通过社交距离策略更新其基因。CHIO使用23个著名的基准函数进行评估。首先，CHIO的参数的敏感性进行了研究。此后，对七个国家的最先进的方法进行了比较评价。比较分析证实，CHIO是能够产生非常有竞争力的结果相比，通过其他行之有效的方法。为了进一步验证，使用了从IEEE-CEC 2011中提取的三个实际工程优化问题。再次证明了CHIO是有效的。总之，CHIO是一个非常强大的优化算法，可用于解决各种优化领域的许多优化问题。
In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.