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Fuzzy-logic adaptive genetic algorithm (FLAGA) in optical design

机译:光学设计中的模糊逻辑自适应遗传算法(Flaga)

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The problem of multiextremum optimization is very general in optical design. Many efforts have been applied to finding approaches to its solution, but optical software developers are still far from finding a universal and reliable method. Solving the problem of finding the optimal angles of rotation of real components is a good way to test different approaches to the problem of multiextremum optimization. Compared with the general approach to optical-system design, multiextremum optimization is unconstrained optimization with an analytical test for the optimization criterion: the mean-square wavefront deformation. Finding optimal angles of rotation shares many specific features with the general problem of optical-system design, such as having a large number of minimums. In addition, these minimums have a special character: they look like a "ravines". The common optimization methods (gradient or Newtonian) easily can find the local minimums associated with an initial point, but they lack the ability to jump to another minimum. A genetic algorithm can find some point in the zone of attraction to another minimum, but it gets stuck in a "ravine" bottom line. An adaptive genetic algorithm together with local optimization methods can find a major number of minimums.
机译:Multi®Tremum优化问题在光学设计中是一般的。已经应用了许多努力来寻找解决方案的方法,但光学软件开发人员仍远未寻找普遍可靠的方法。解决找到真实组件旋转的最佳角度的问题是测试多种文献优化问题的不同方法的好方法。与光学系统设计的一般方法相比,Multiextremum优化是针对优化标准的分析测试的无约束优化:平均方形波前变形。找到最佳旋转角度与光学系统设计的一般问题共享许多特定功能,例如具有大量最小值。此外,这些最低限度有一个特殊的性格:它们看起来像一个“沟壑”。常见的优化方法(梯度或牛顿)容易找到与初始点相关的局部最小值,但它们缺乏跳转到另一个最小值的能力。遗传算法可以在吸引区域到另一个最小值,但它卡在“山沟”的底线中。一种自适应遗传算法以及本地优化方法可以找到主要的最小值。

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