A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization

In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden statistical significance of these metaheuristics aid future developments. This focuses on six metaheuristics, namely, ant lion optimization (ALO), arithmetic algorithm (AOA), dragonfly (DA), grey wolf optimizer (GWO), salp swarm (SSA) whale (WOA). Optimization an industrial machining application tackled in this paper. The optimal parameters (peak current, duty factor, wire tension water pressure) WEDM are predicted using aforementioned metaheuristics. objective functions maximize material removal rate (MRR) minimize wear ratio (WR) surface roughness (SR). All current seen surpass existing results, thereby indicating their superiority over conventional algorithms.