Piggybacking on past problem for faster optimization in aluminum electrolysis process design

In process industry, it is a habitual phenomenon that the production efficiency is improved via replacing the obsolete devices with the advanced ones. To achieve an optimal performance, these devices are always required for heavily empirical adjustments, which is very time-consuming and inefficient. Though outdated, invaluable experiences for adjusting devices towards a more efficient industrial production have been accumulated by those replaced facilities. Thereby, new devices are expected to adapt to perform the industrial task without many modulations if the aforementioned experiences can be appropriately utilized. Inspired by the fact that evolutionary multitasking is capable of exploiting latent similarities and commonalities among multiple optimization tasks so as to improve the overall convergence of multi-task optimization, in this paper, we propose a novel framework to automatically search for the optimal settings for new devices based on the knowledge accumulated by the old. The framework, dubbed Piggybacking on Past Problem for Faster Optimization (PPPFO), is able to piggyback on the past optimization problem for a faster convergence of the targeted. By means of automatically transferring search experiences (i.e. genetic and cultural characteristics) from source task to the target, PPPFO can assist an engineering optimizer to improve its search exercises. PPPFO has been tested with a number of widely used benchmark functions and has been successfully adopted to an important real application, i.e., aluminum electrolysis process design. The remarkable results verify the efficacy and efficiency of the proposed framework.