Evolutionary Multitask Optimization in Real-World Applications: A Survey

Abstract

Because of its strong ability to solve problems, evolutionary multitask optimization (EMTO) algorithms have been widely studied recently. Evolutionary algorithms have the advantage of fast searching for the optimal solution, but it is easy to fall into local optimum and diffificult to generalize. Combining evolutionary multitask algorithms with evolutionary optimization algorithms can be an effective method for solving these problems. Through the implicit parallelism of tasks themselves and the knowledge transfer between tasks, more promising individual algorithms 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 explores the existing evolutionary multitasking theory and improvement scheme in detail. Then, it summarizes the application of EMTO in different scenarios. Finally, according to the existing research, the future research trends and potential exploration directions are revealed.