The film-covered sweet potato transplanting method requires ensuring the transplantation conditions of small planting holes and large lateral displacement.In the soil insertion phase,the transplantation machine requir...The film-covered sweet potato transplanting method requires ensuring the transplantation conditions of small planting holes and large lateral displacement.In the soil insertion phase,the transplantation machine requires a mechanism design with multiple timed poses,and the existing design methods are still imperfect.For this reason,this article proposes a multi-target trust region parameter-guided optimization algorithm.This algorithm aims to achieve multi-objective optimization design with more timed pose conditions starting from individual timed pose conditions.First,multi-target problems are decomposed into multiple subproblems,and the parameter arrays are kept with the minimum polymerization value of each subproblem.Then,the approximate function value reduction for each target is calculated using this parameter set,and the step size for the next iteration of each subproblem is determined by comparing this approximate reduction with the actual reduction.After many iteration calculations,the parameter arrays end the calculation when the parameter group is no longer updated.This paper uses the design of a film-covered sweet potato transplanting mechanism as a complex optimized application example.The algorithm is used to obtain the optimization results of the target values of eight groups of institutions.The smallest hole is 2.99 mm,and the horizontal transplanting distance is 108.40 mm.The maximum hole is 17.64 mm,and the horizontal transplanting distance is 124.97 mm.Considering the size of the hole and the horizontal transplanting distance of sweet potato transplanting,the mechanism’s target value of the horizontal transplanting distance at 119.92 mm and the hole size at 0.31 mm were selected to design the sweet potato transplanting machine.The correctness of the results is verified,which reflects the practicability of the algorithm.展开更多
基金supported by the Key Research Projects of China(Grant No.2022YFD2001800)the National Natural Science Foundation of China(Grant No.32071909)Zhejiang Provincial Natural Science Foundation(Grant No.LD24E05007).
文摘The film-covered sweet potato transplanting method requires ensuring the transplantation conditions of small planting holes and large lateral displacement.In the soil insertion phase,the transplantation machine requires a mechanism design with multiple timed poses,and the existing design methods are still imperfect.For this reason,this article proposes a multi-target trust region parameter-guided optimization algorithm.This algorithm aims to achieve multi-objective optimization design with more timed pose conditions starting from individual timed pose conditions.First,multi-target problems are decomposed into multiple subproblems,and the parameter arrays are kept with the minimum polymerization value of each subproblem.Then,the approximate function value reduction for each target is calculated using this parameter set,and the step size for the next iteration of each subproblem is determined by comparing this approximate reduction with the actual reduction.After many iteration calculations,the parameter arrays end the calculation when the parameter group is no longer updated.This paper uses the design of a film-covered sweet potato transplanting mechanism as a complex optimized application example.The algorithm is used to obtain the optimization results of the target values of eight groups of institutions.The smallest hole is 2.99 mm,and the horizontal transplanting distance is 108.40 mm.The maximum hole is 17.64 mm,and the horizontal transplanting distance is 124.97 mm.Considering the size of the hole and the horizontal transplanting distance of sweet potato transplanting,the mechanism’s target value of the horizontal transplanting distance at 119.92 mm and the hole size at 0.31 mm were selected to design the sweet potato transplanting machine.The correctness of the results is verified,which reflects the practicability of the algorithm.
