ABSTRACT
The ant lion optimization (ALO) algorithm mimics the hunting mechanism of ant lions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and rebuilding traps are implemented. Ants update their positions with random walk at every step of optimization. Elitism is an important characteristic of evolutionary algorithms that allows them to maintain the best solution obtained at any stage of the optimization process. This chapter discusses thickness gradient which has been selected as the performance measure. Experiments are designed to study and correlate the effects of process parameters on uniform thickness distribution. 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.
