Elephant Swarm Water Search Algorithm-Based Optimization of a Laser Beam Machining Process

Laser beam machining (LBM) is a popular non-conventional material removal process. It is observed that quality of cut in LBM process performance can only be achieved through proper tuning of its different input parameters. It is thus vital to explore the effects of LBM process parameters on the quality of cut. In this paper, a newly developed metaheuristic algorithm, i.e. elephant swarm water search algorithm (ESWSA), inspired by the behavior of social elephants, is employed to determine the optimal combination of gas pressure ( \(P_{a}\) ), pulse width ( \(W_{p}\) ), pulse frequency ( \(f_{p}\) ) and cutting speed ( \(S_{c}\) ) during Nd:YAG laser-based straight profile cutting of thin aluminium alloy sheet. During multi-objective optimization, minimum values of kerf taper (KT) and average surface roughness ( \(Ra\) ) are achieved as 0.294° and 0.133 μm respectively at a parametric combination of \(P_{a}\) = 7.437 kg/cm2, \(W_{p}\) = 1.6 ms, \(f_{p}\) = 8 Hz and \(S_{c}\) = 6 mm/min while providing equal importance to both the responses. It is noticed that for both single and multi-objective optimization problems, ESWSA supersedes its peers, like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC) and differential evolution (DE) with respect to accuracy and deviation of the derived solutions, and computational effort. Application of ESWSA achieves 8.41, 2.00, 14.53, 27.76 and 2.32%; and 44.81, 55.52, 18.90, 29.63 and 60.42% improvements respectively for KT and \(Ra\) against ABC, ACO, PSO, DE and GA techniques when both the responses are equally preferred.