Enhancing performance of cell formation problem using hybrid efficient swarm optimization

Cellular manufacturing design is apprehensive about the conception and activity of cells to take the benefits of adaptability, effective flow, and high creation rate. The way toward forming manufacturing cells with the greatest efficiency is the most critical strides in cellular manufacturing. In this paper, a new monarch butterfly optimization (MBO) and firefly (FF)-based meta-heuristic is proposed to solve a multi-objective cell formation problem (CFP). This hybridized MBO–FF acquires optimal arrangements in a worthy measure of time, particularly for big size problems also focused to enhance the working of CFP. This algorithm is competent to investigate the search space viably and recognize the global optimal within a short measure of time. Here, percentage of exceptional elements, machine utilization, grouping efficacy and cell efficiency are measured for the performance enhancement. Computational outcome of the presented MBO–FF herein demonstrates superior or equivalent to the benchmark instance collected from the literature.