AMulti-Factorial Evolutionary AlgorithmWith AsynchronousOptimization Processes for Solving the Robust Influence Maximization Problem Shuai Wang and Beichen Ding Sun Yat-sen University, CHINA YaochuJin Bielefeld University, GERMANY Abstract-The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading Digital Object Identifier 10.1109/MCI.2023.3277770 Date ofcurrent version: 13July 2023 1556-603X ß 2023 IEEE ability, the influence maximization problem is ofgreat significance in network-based information diffusion tasks. Plenty of attention has been paid to simulating the diffusion process and choosing influential seeds. However, errors and attacks typically threaten the normal function ofnetworked systems, and few studies have considered the influence maximization problem under structural failures. Therefore, a quantitative measure with a changeable parameter is first developed in this paper to tackle the unpredictable destruction percentage on networks. Further, limitations on the existing methods are shown experimentally. To Corresponding author: Yaochu Jin (e-mail: yaochu.jin@uni-bielefeld.de). AUGUST 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 41 ©SHUTTERSTOCK.COM/PROSTOCKSTUDIOhttp://www.SHUTTERSTOCK.COM/PROSTOCKSTUDIO https://orcid.org/0000-0002-1796-7280 https://orcid.org/0000-0002-5771-1204 https://orcid.org/0000-0003-1100-0631