What makes evolutionary multi-task optimization better: A comprehensive survey

Abstract

Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA) that aims to optimize multiple tasks simultaneously within a same problem and output the best solution for each task. EMTO utilizes the strengths of EA to perform global optimization without relying on the mathematical properties of the problem. Therefore, EMTO is particularly suitable for complex, non-convex and nonlinear problems. Unlike traditional single-task EA, EMTO can deal with multiple optimization problems at once and can automatically transfer knowledge among these different problems. EMTO provides a novel approach for solving multi-task optimization problems and has attracted the attention of many researchers in the field of evolution. Due to the strong parallel search capability of EMTO, many excellent theoretical and applied research has been proposed on EMTO. To better organize these respectable research works and inspire future researchers, this paper reviews the related works on EMTO in the following three aspects. Firstly, many works focus on improving the performance of EMTO through various optimization strategies. Through an in-depth analysis and review of the current literature on this topic, we provide a comprehensive summary of these strategies. Secondly, we provide examples of real-world applications of EMTO, as well as its combination with other optimization paradigms. These examples demonstrate the wide applicability of EMTO. Finally, we propose some potential directions for future research in EMTO to inspire researchers in this field.