A self-adaptive chaotic differential evolution for forward displacement analysis of Stewart mechanism
Differential evolution(DE)algorithm has gained increasing attention in handling complex optimal problems.A self-adaptive chaotic escape based DE algorithm(SaCDE)for global optimization is proposed to improve the performance of the canonical DE.With the evolution of the generations,a dynamic chaotic immigrant technique is incorporated into the traditional DE to enhance the population diversity at early stages to explore the more promising domains and keep intensification for solution with higher accuracy in the later stages during the optimization.This work utilizes the vector method to obtain geometrical constrained equations for positive positional solution to mechanisms and then transform them into unconstrained optimal problems,which are solved subsequently by the SaCDE.Experiments on the numerical example show that this new method is able to obtain all real solutions to forward displacement analysis of Stewart parallel.Compared with the canonical DEs and particle swarm optimization(PSO),the SaCDE is more effective and efficient in terms of the optimal solution quality and the success rate on the example.
differential evolution algorithm chaotic immigration forward displacement analysis parallel mechanism
Linxian Che Rong Zhang Taofen Wang
School of Mechanical Engineering,Chongqing Vocational Institute of Engineering,Chongqing 402260,Chin School of Mechanical Engineering,Chongqing Vocational Institute of Engineering,Chongqing 402260,Chin
国内会议
北京
英文
157-166
2018-08-02（万方平台首次上网日期，不代表论文的发表时间）