Enhanced Greylag Goose optimizer for solving constrained engineering design problems

Abstract This paper introduces an improved optimization algorithm based on migration patterns of greylag geese, known for their efficient flying formations. The Modified Greylag Goose Optimization Algorithm (MGGOA) is modified by augmenting the levy flight mechanism and artificial neural network (ANN) strategies. The algorithm is detailed, presenting mathematical formulations for both phases. Subsequently, the paper applies the MGGOA to various engineering optimization problems, including heat exchanger design, car side impact design, spring design optimization, disc clutch brake optimization, and structural optimization of an automobile component. Statistical comparisons with benchmark algorithms demonstrate the efficacy of MGGOA in finding optimal solutions for these design engineering problems.