Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm

Enumerative Optimization Procedure for the Gear Train Optimization Problem of a Two-Speed Dedicated Electric Transmission

“…The results showed that the PSOA and the SAA were more effective and efficient than the GA. However, numerous trials, especially for inexperienced designers, might have to be performed to appropriately determine several parameters for the advanced stochastic methods used in those studies [16][17][18][19][20][21][22][23] since the efficacies of those methods significantly depend on such parameters. Thus, such a requirement might actually increase the total times for those methods to obtain global optima.…”

“…The enumeration technique is preferred to the random methods [12][13][14][15] and the advanced stochastic methods [16][17][18][19][20][21][22][23] because the number of the possible solutions to the 2DET could be well limited considering practical manufacturing requirements and a high-performance computer could scan and evaluate all the candidates efficiently. In addition, multiple criteria are simultaneously optimized for the 2DET using the MMPO to find global optima since the MMPO could well represent the real optimization purposes of designers in an intuitive, reasonable and comprehensive way.…”

“…Yokota et al [16] optimized the weight of a gear set with an improved GA. Marcelin [17] proposed a GA combined with a penalty selection method to optimize gear pairs. Mendi et al [18] used a GA with a static penalty function incorporated in the fitness function to minimize the volume of a single-stage gearbox. Gologlu and Zeyveli [19] utilized a GA to minimize the total volume of a two-stage GT, with static and dynamic penalty function methods implemented to handle constraints.…”

Volume optimization of gear trains with spur gears using genetic algorithm

“…Al [3] presented gear train volume/weight minimization optimizing single and multistage gear trains' gear ratios. Mendi et al [4] aimed to optimize gear train component dimensions to achieve minimal volume comparing GA results to analytic method parameter volume. Savsani et al [5] described gear train weight optimization comparing various optimization methods to genetic algorithm (GA) result values.…”

A Multi-objective Optimization Approach for Design of Worm and Worm Wheel Based on Genetic Algorithm

“…When the number of design parameters increases, the complexity increases drastically. If the optimization problem involves the objective function and constraints that are not stated as explicit functions of the design variables or which are too complicated to manipulate, it is hard to solve by classical optimization methods [2]. [3].…”