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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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摘要

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:在光学设计中,多重极值优化的问题非常普遍。人们已经为找到解决方案的方法付出了很多努力,但是光学软件开发人员离找到一种通用且可靠的方法还很遥远。解决找到实际组件的最佳旋转角度的问题是测试多极值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,多极值优化是无约束优化,并通过分析测试确定了优化标准:均方波前变形。寻找最佳旋转角度与光学系统设计的一般问题具有许多特定特征,例如具有大量的最小值。另外,这些最小值具有特殊的特征,它们看起来像一个“沟壑”。常用的优化方法(梯度方法或牛顿方法)可以轻松找到与初始点关联的局部最小值,但是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中找到另一个最小的点,但是它陷入了“沟壑”的底线。自适应遗传算法与局部优化方法一起可以找到大量的最小值。

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