A Knowledge Sharing and Individually Guided Evolutionary Algorithm for Multi-Task Optimization Problems

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