Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics

In this paper, nine recent meta-heuristics have been employed to search for optimal design of an automatic planetary gear train. The function of the designed system is to automatically transmit power and motion in automobiles. Nine mixed decision parameters are considered in the optimisation procedure. The geometric conditions such as the undercutting, the maximum overall diameter of the transmission, as well as the spacing of multiple planets are taken into account to ensure an optimum design. All the above algorithms are tested both quantitatively and qualitatively for solution quality, robustness, and their time complexity is determined. Results obtained illustrate that the utilised approaches can effectively solve the planetary gearbox problem. Besides this, the comparative study indicates that roulette wheel selection-elitist differential evolution (ReDE) outperforms the other algorithms in terms of the statistical results, and FA has the best convergence behaviour. Meanwhile, multi-verse optimisation (MVO) and butterfly optimisation algorithm (BOA) performed better than the other used algorithms when computation time was considered.