Adaptive Auxiliary Task Selection for MultitaskingAssisted Constrained MultiObjective Optimization Fei Ming and Wenyin Gong China University ofGeosciences, CHINA Liang Gao Huazhong University ofScience, and Technology, CHINA Abstract-Solving constrained multi-objective optimization problems (CMOPs) is one of the most popular research topics in the multi-objective optimization community. Various approaches based on different algorithmic strategies have been proposed for benchmark CMOPs with different features and challenges. Although most of these algorithms employ one or more fixed strategies, determining the most suitable strategy is challenging and problem-dependent. This work presents a general multitasking Digital Object Identifier 10.1109/MCI.2023.3245719 Date ofcurrent version: 6April 2023 18 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2023 framework comprising a main task for the original CMOP and an arbitrary number of auxiliary tasks based on different strategies for the auxiliary problems. Reinforcement learning techniques are utilized according to the population state to choose the most suitable auxiliary task during the evolutionary process. As instantiations, Q-Learning and Deep Q-Learning are successfully applied in the framework. Accordingly, two novel algorithms are developed to solve CMOPs. The results demonstrate the effectiveness of the proposed approaches on five benchmark CMOP test suites and real-world problems compared to 11 advanced methods. I. Introduction S olving constrained multi-objective optimization problems (CMOPs) is one of the most popular research topics among the multi-objective optimization problems (MOPs) community due to the Corresponding author: Wenyin Gong (e-mail: wygong@cug.edu.cn). 1556-603X ß 2023 IEEE IMAGE LICENSED BY INGRAM PUBLISHINGhttps://orcid.org/0000-0003-1042-8206 https://orcid.org/0000-0003-1610-6865 https://orcid.org/0000-0002-1485-0722