No abstract

Multi-task optimization (MTO) is a novel emerging evolutionary computation paradigm. It focuses on solving multiple optimization tasks concurrently while improving optimization performance by utilizing similarities among tasks and historical optimization knowledge. To ensure its high performance, it is important to choose proper individuals for each task. Most MTO algorithms limit each individual to one task, which weakens the effects of information exchange. To improve the efficiency of knowledge transfer and choose more suitable individuals to learn from other tasks, this work proposes a general MTO framework named individually guided multi-task optimization (IMTO). It divides evolutions into vertical and horizontal ones, and each individual is fully explored to learn experience from the execution of other tasks. By using the concept of skill membership, individuals with higher solving ability are selected. Besides, to further improve the effect of knowledge transfer, only inferior individuals are selected to learn from other tasks at each generation. The significant advantage of IMTO over the multifactorial evolutionary framework and baseline solvers is verified via a series of benchmark studies.