基于改进元胞遗传算法的蜗杆传动多目标优化设计

Multi-optimization Design of Worm Transmission Based on Improved Cellular Genetic Algorithm

建立了以蜗轮的齿冠体积最小、相对滑动速度最小和中心距最小为目标函数,以满足蜗轮蜗杆强度和刚度等条件为约束条件的多目标优化模型,提出了一种自适应差分进化的多目标元胞遗传算法。该算法针对多目标元胞遗传算法的特点,结合差分进化中不同进化策略的特性,将两种不同的进化策略融合为一种新的差分进化策略,得到一种参数自适应控制的多目标元胞差分遗传算法。将该算法同其他典型的多目标进化算法在标准测试函数上进行性能对比试验,结果显示所提出的算法相比其他算法具有更好的收敛性和分布性。工程实例的求解证明了该算法可有效解决相关实际问题。

Taking the minimization of the tooth crown of a worm wheel and the relative sliding speed transmission efficiency and the distance from the center as objectives, established the model for the multi-objec-tive optimization under that the worm transmission meet the needs for strength and stiffness and others. An adaptive differential evolution of multi-objective cellular genetic algorithm was proposed. Considering the feature of different differential evolution strategies, a new differential strategy with parameter adaptive-control was formed. Aiming at the characteristics of cellular multi-objective optimization algorithm, the new improved strategy was integrated into the algorithm. The comparative performance test results reveal that the proposed algorithm outperforms some state-of-the-art algorithms in terms of convergence and diversity. The engineering example proved the algorithm could solve the relevant practical problems effectively.