An adaptive penalty function-based maximin desirability index for close tolerance multiple-response optimization problems

Simultaneous optimization of multiple-quality characteristics and determining the process settings is a critical and difficult task for practitioners. Such types of problems are generally referred to as “multiple-response optimization” problems. To handle high-dimensional multiple-response problems, a popular strategy, using desirability functions, is recommended by various researchers. Various types of desirability index functions are recommended to convert multiple scale-free desirability measures to a single composite desirability (or single objective) value. Thus, the objective is then to maximize the single composite desirability for a specific problem. In this paper, a new adaptive penalty function-based “maximin” desirability index is proposed, which provide superior solution as compared to existing maximin approach, for close (or tight) engineering tolerances of response characteristics. The superiority was proved based on statistical comparison using varied case situations and different swarm intelligent search strategies.