并联机构位置正解的自适应差分进化算法

发布时间：2019-05-06 08:03

【摘要】：根据杆长约束条件,建立求6自由度一般6-SPS并联机构位置正解的无约束优化模型,再应用差分进化(Differential evolution,DE)算法求解该问题。针对基本DE算法可能出现进化停滞或陷入局部极值区域的缺点,提出一种引入新个体的自适应策略,以增强算法全局优化性能。将引入新个体的自适应策略融入DE算法,并使用混合变异算子及基于三角函数扰动的缩放因子和交叉因子,形成自适应差分进化(Adaptive DE,ADE)算法。数值结果表明,对于一般6-SPS并联机构正运动学分析问题,ADE算法能以较少计算开销求出全部高精度位置正解。通过与基本DE算法、自适应变异粒子群算法和改进人工蜂群算法比较,验证了ADE算法的收敛精度和计算稳健性指标优于对比算法。
[Abstract]:According to the constraint condition of bar length, an unconstrained optimization model for solving the forward position solution of 6-DOF general 6-SPS parallel mechanism is established, and then the differential evolution (Differential evolution,DE) algorithm is applied to solve the problem. In order to improve the global optimization performance of the basic DE algorithm, an adaptive strategy is proposed to improve the global optimization performance of the algorithm, which may lead to the stagnation of evolution or fall into the region of local extremum. The adaptive differential evolution (Adaptive DE,ADE) algorithm is formed by introducing the adaptive strategy of new individuals into the DE algorithm and using hybrid mutation operator and scaling factor and cross factor based on trigonometric function disturbance to form an adaptive differential evolution (Adaptive DE,ADE) algorithm. The numerical results show that for the forward kinematics analysis of the general 6-SPS parallel mechanism, the ADE algorithm can obtain all high-precision forward position solutions with less computational overhead. Compared with the basic DE algorithm, the adaptive mutation particle swarm optimization algorithm and the improved artificial bee swarm algorithm, the convergence accuracy and computational robustness of the ADE algorithm are proved to be better than that of the contrast algorithm.
【作者单位】：泸州职业技术学院机械工程系;重庆工商大学制造装备机构设计与控制重庆市重点实验室;
【基金】：重庆市基础科学与前沿技术研究专项(cstc2015jcyj A70006) 重庆市教育委员会科学技术研究项目(KJ1403201) 制造装备机构设计与控制重庆市重点实验室开放基金资助项目(611115006) 泸州市科学技术与知识产权局科学技术研究项目(2012-S-43)
【分类号】：TH112

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