ABSTRACT
The gray wolf optimizer (GWO) algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature. Four types of gray wolves—alpha, beta, delta, and omega—are employed for simulating the leadership hierarchy. This chapter talks about the interior penalty function method which transforms any constrained optimization problem into an unconstrained one. However, the barrier functions prevent the current solution from ever leaving the feasible region. The chapter discusses linear mathematical relations have been developed from the results of Taguchi design of experiments and analysis of variance between input parameters like blank-holder force, friction coefficient, die profile radius, and punch nose radius. The performance characteristics applied for punch plate is thinning. During the minimization process, the diameter of the punch plate and process variables such as die profile radius and coefficient of friction were selected.
