Many-task optimization algorithm based on adaptive dual knowledge transfer

In real-world optimization problems, it is crucial to effectively exploit the synergies among tasks. Recent advances in evolutionary many-task optimization have highlighted adaptive knowledge transfer, particularly concerning transfer frequency and auxiliary task selection. However, the prevailing methods have some limitations, including complex parameter setting for auxiliary task selection and the inaccuracy of population-based task similarity measurement. This article proposes an adaptive many-task optimization algorithm with dual knowledge transfer (MTO-ADKT). First, an enhanced multi-armed bandit mechanism automates the selection of source tasks using a minimum of parameters. Secondly, the timing of knowledge transfer is optimized through dynamic adjustment of the transfer frequency. Thirdly, an elite-driven adaptation strategy enhances the precision of knowledge transfer. Through extensive experimentation, the effectiveness of MTO-ADKT was validated on four benchmark test sets with two, 10 and 50 tasks, and a planar manipulator control problem with 500 tasks, demonstrating its superior performance to seven state-of-the-art algorithms.