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A metaheuristic approach for optical network optimization problems

机译:光网络优化问题的元启发式方法

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Two of the most complex optimization problems encountered in the design of third generation optical networks are the dynamic routing and wavelength assignment (DRWA) problem under the assumptions of ideal and non-ideal physical layers. Both these problems are NP-complete in nature. These are challenging due to the presence of multiple local optima in the search space. Even heuristics-based algorithms fail to solve these problems efficiently as the search space is non-convex. This paper reports the performance of a metaheuristic, that is, an evolutionary programming algorithm in solving different optical network optimization problems. The primary motivation behind adopting this approach is to reduce the algorithm execution time. It is demonstrated that the same basic approach can be used to solve different optimization problems by designing problem-specific fitness functions. Also, it is shown how the algorithm performance can be improved by integrating suitable soft constraints with the original constraints. Exhaustive simulation studies are carried out assuming the presence of different levels of linear impairments such as switch and demultiplexer crosstalk and non-linear impairments like four wave mixing to illustrate the superiority of the proposed algorithms.
机译:在第三代光网络设计中遇到的两个最复杂的优化问题是在理想和非理想物理层假设下的动态路由和波长分配(DRWA)问题。这两个问题本质上都是NP完全的。由于搜索空间中存在多个局部最优值,因此具有挑战性。甚至基于启发式的算法也无法有效解决这些问题,因为搜索空间是非凸的。本文报告了一种元启发式算法(即进化规划算法)在解决不同的光网络优化问题方面的性能。采用这种方法的主要动机是减少算法执行时间。通过设计特定问题的适应度函数,可以证明相同的基本方法可用于解决不同的优化问题。此外,还显示了如何通过将合适的软约束与原始约束集成来提高算法性能。进行了详尽的仿真研究,假设存在不同程度的线性损伤,例如开关和解复用器串扰以及非线性损伤(例如四波混频),以说明所提出算法的优越性。

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