Parametric optimization of abrasive water-jet machining processes using grey wolf optimizer

Abrasive water-jet machining (AWJM) is a hybrid advanced machining process, which can be economically applied to machine almost any kind of material. It employs a high velocity waterjet to propel abrasive particles through a nozzle on the workpiece surface for material removal. The machining performance of AWJM process naturally depends on its several control (input) parameters, like water pressure, nozzle diameter, jet velocity, abrasive concentration, nozzle tip distance etc., which have also predominant effects on its responses, i.e., material removal rate, surface roughness, overcut, taper etc. In this paper, a new evolutionary algorithm, i.e., grey wolf optimizer (GWO), a technique based on the hunting behavior of grey wolves, is applied for finding out the optimal parametric combinations of AWJM processes. The main advantage of this algorithm is that it does not accumulate towards some local optima, and the presence of a social hierarchy helps it in storing the best possible solutions obtained so far. The derived results using GWO exhibit a significant improvement in the response values as compared to the previous attempts for parametric optimization of AWJM processes while applying other algorithms.     

Truss topology,shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution

A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article. Hybrid reproduction and control parameter adaptation strategies are employed to increase the performance of the algorithm. The proposed algorithm, called fully stressed design–grey wolf–adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems. The problems have mixed continuous/discrete design variables that are assigned as simultaneous topology, shape and sizing design variables. FSD-GWADE provides competitive results and gives superior results at a higher success rate than the previous FSD-based algorithm.