Abstract
Due to its good ability to solve problems, evolutionary multitask optimization (EMTO) algorithm has been widely studied recently. Evolutionary algorithm has the advantage of fast searching for the optimal solution, but it is easy to fall into local optimum and difficult to generalize. To solve these problems, it is an effective method to combine with multitask optimization algorithm. Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks, more promising individuals can be generated in the evolution process, which can jump out of the local optimum. How to better combine the two has also been studied more and more. This paper will explore the existing evolutionary multitasking theory and improvement scheme in detail. Then it summarizes the application of evolutionary multitask optimization in different scenarios. Finally, according to the existing research, the future research trends and potential exploration directions are revealed.
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