Multi-objective Grasshopper Optimizer for Improved Machining Performance

One of the most popular meta-heuristic optimization algorithms today is the grasshopper algorithm. Several industries, including engineering design, wireless networking, machine learning and control of power systems have effectively used it to solve a variety of optimization challenges. Despite its potential for optimization, grasshopper optimizer algorithm has not been used for machining performance. Therefore, the present work is the first study that utilized grasshopper algorithm to optimize the cutting force and the surface roughness simultaneously in a multi-objective approach. In order to illustrate the approach proposed in this study, quadratic equations were extracted from an existing study. The speed of cutting, the feed rate and the depth of cut were the input factors. Details showing the formulation of the problem and the results have been disclosed. The Multi-objective grasshopper optimizer (MOGOA) was the metaheuristic-based approach proposed for the formulation of the problem and the computation of the non-dominated or Pareto optimal solutions. The best compromise was obtained by optimizing these two objectives simultaneously. The optimal settings corresponding to the optimal values of cutting force and surface roughness were computed and reported in this section.