The comparison of metaheuristic algorithms in parametric optimization of laser-based processes

Abstract

The paper presents parametric optimization of two heterogeneous laser-based processes with multiple outputs (laser drilling and laser shock peening), using an advanced optimisation methodology. The data obtained from experimentations are processed by statistical method to address requirements for multiple responses, and then the process interrelationships are mapped using artificial neural networks. The neural process model is fed into three major metaheuristic algorithms whose effectiveness are benchmarked: genetic algorithm, simulated annealing and particle swarm optimization. The results are discussed in terms of the quality of the obtained solutions and convergence rate, as well as the influence of the algorithm's hyper-parameters on the obtained results. The adopted optimization results are successfully verified in practice. The recommendations for the metaheuristic algorithm selection and guidelines for the algorithm settings are drawn.