An effective variable transfer strategy in multitasking optimization

As an emerging paradigm in evolutionary computation, multitasking evolutionary algorithm can solve multiple self-contained tasks simultaneously. Its performance has largely relied on task similarity. In this paper, a novel variable transfer strategy is proposed to boost the task similarity and reduce negative transfer of useful knowledge between tasks. The experiment results on nine instances revealed that, the proposed strategy has great exploitation ability, enhancing the convergence speed.