针对冠状病毒群体免疫优化(CHIO)算法收敛速度慢、求解精度低的问题,提出一种基于群体划分的冠状病毒群体免疫优化(SD-CHIO)算法.基于适应度均匀原则将初始群体划分为两部分,即全局寻优个体与局部寻优个体.对于全局寻优个体,在其位置更新中加入差分变异与漫反射变异策略,分别用来增强全局寻优个体之间的交流与群体多样性,从而提高算法的全局搜索能力.对于局部寻优个体,在其位置更新中引入一种自适应快速收敛策略:基于增量法进行精英预测,并加入一种自适应收敛系数使局部寻优个体能快速收敛至精英解,以提升算法的局部搜索能力.数值实验表明:SD-CHIO能够有效提高原算法的收敛速度与精度,并表现出明显优于其他元启发式算法的全局与局部搜索能力以及一定的工程价值.

Aimed at the drawbacks of coronavirus herd immunity optimizer (CHIO), i.e., slow convergence speed and low optimization accuracy, a CHIO based on swarm division (SD-CHIO) is proposed. Based on the principle of uniform fitness, the initial swarm is divided into two parts, i.e., exploration individuals and exploitation individuals. For exploration individuals, differential mutation and diffuse reflection mutation are adopted in position update in order to enhance the communication among exploration individuals and swarm diversity respectively, so as to improve the exploration capability of the algorithm. For exploitation individuals, an adaptive fast convergence strategy is proposed in position update: elite prediction is conducted based on the incremental method, and an adaptive convergence coefficient is employed to ensure that exploitation individuals can quickly converge to the elite solution, which improves the exploitation capability of the algorithm. The numerical experiments demonstrate that SD-CHIO significantly improves the convergence speed and accuracy of the conventional algorithm, exhibiting better exploration and exploitation capabilities than other meta-heuristic algorithms do as well as certain value in engineering.