Xiaoliang Ma, Yongjin Zheng, and Zexuan Zhu Shenzhen University, CHINA Xiaodong Li RMIT University, AUSTRALIA Lei Wang Chinese Academy of Sciences, CHINA Yutao Qi Xidian University, CHINA Junshan Yang Dalian University of Foreign Languages, CHINA Digital Object Identifier 10.1109/MCI.2021.3108311 Date of current version: 13 October 2021 38 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2021 Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation Abstract-Solving a complex optimization task from scratch can be significantly expensive and/or time-consuming. Common knowledge obtained from different (but possibly related) optimization tasks may help enhance the solving of such tasks. In this regard, evolutionary multitasking optimization (EMTO) has been proposed to improve the solving of multiple optimization tasks simultaneously via knowledge transfer in the evolutionary algorithm framework. The effectiveness of knowledge transfer is crucial for the success of EMTO. Multifactorial evolutionary algorithm (MFEA) is one of the most representative EMTO Corresponding Author: Zexuan Zhu (e-mail: zhuzx@szu.edu.cn). 1556-603X/21©2021IEEE ©SHUTTERSTOCK.COM/TADAMICHIhttp://www.SHUTTERSTOCK.COM/TADAMICHI