A Coronavirus Herd Immunity Optimizer Based on Swarm Division

Abstract

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.

Keywords

• coronavirus herd immunity optimizer (chio)
• swarm division
• adaptive fast convergence
• differential mutation
• diffuse reflection mutation