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国外文献 > 1.轴承 > 4. 设计与分析 > 算法与优化

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标 题： Application of the genetic algorithm to the multi-objective optimization of air bearings
遗传算法在空气轴承多目标优化中的应用
作 者： Nenzi Wang
Yau-Zen Chang
关键词： Genetic algorithm  [遗传算法]
Optimization  [ 优化]
Air bearings  [ 空气轴承]
出版年： 2004
期刊名： Tribology Letters
摩擦学通讯
年卷期： 2004, vol.17, no.2
摘 要： A feasible solution must be obtained in a reasonable time with high probability of global optimum for a complex tribologi-cal design problem, To meet this decisive requirement in a multi-objective optimization problem, the popular and powerful genetic algorithms (GAs) are adopted in an illustrated air bearing design. In this study, the goal of multi-objective optimization is achieved by incorporating the criterion of Pareto optimality in the selection of mating groups in the GAs, In the illustrated example the diversity of group members in the evolution process is much better maintained by using Pareto ranking method than that with the roulette wheel selection scheme. The final selection of the optimal point of the points satisfied the Pareto optimality is based on the minimum-maximum objective deviation criterion, It is shown that the application of the GA with the Pareto ranking is especially useful in dealing with multi-objective optimizations A hybrid selection scheme combining the Pareto ranking and roulette wheel selections is also presented to deal with a problem with a combined single objective With the early generations running the Pareto ranking criterion, the resultant divergence preserved in the population benefits the overall GA's performance. The presented procedure is readily adoptable for parallel computing, which deserves further study in tribological designs to improve the computational efficiency.

对于复杂的摩擦学设计问题，必须在合理的时间内获得一个可行解，并且有较高的概率达到全局最优。为了满足多目标优化问题中的这一关键要求，在一个示例的空气轴承设计中采用了广受欢迎且功能强大的遗传算法（GA）。本研究通过在GA的配对组选择中引入帕累托最优性准则，实现了多目标优化的目标。在该示例中，采用帕累托排序方法比轮盘赌选择方案更能有效保持进化过程中的群体成员多样性。最终选择出的最优解点基于最小-最大目标偏差准则，满足了帕累托最优性要求。研究表明，将帕累托排序与遗传算法结合应用，特别适用于处理多目标优化问题。同时，还提出了一种混合选择方案，结合帕累托排序和轮盘赌选择，以应对具有复合单目标的问题。在早期迭代阶段，采用帕累托排序准则运行。种群中保留的这种结果性差异有利于整体遗传算法的性能。所提出的方案易于应用于并行计算，值得在摩擦学设计中进一步研究，以提高计算效率。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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