In response to many multi-attribute decision-making (MADM) problems involved in chemical processes such as controller tuning

which suffer human's subjective preferential nature in human-computer interactions

a novel affective computing and preferential evolutionary solution is proposed to adapt human-computer interaction mechanism. Based on the stimulating response mechanism

an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing. In addition

the mathematical relationship between affective space and decision maker's preferences is constructed. Subsequently

a human-computer interactive preferential evolutionary algorithm for MADM problems is proposed

which deals with attribute weights and optimal solutions based on preferential evolution metrics. To exemplify applications of the proposedmethods

some test functions and

emphatically

controller tuning issues associated with a chemical process are investigated

giving satisfactory results.