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The design of optical networks is frequently accomplished by using evolutionary algorithms (EAs). However, the overall optimization process presents a huge execution time. In this letter, we propose a new method based on factorial design and geographical graph models to design optical networks. We propose an iterative graph generator based on Gabriel graphs that considers the number of deployed fibers and traffic demand. We focus on the specification of the fiber topology and optical devices aiming at finding a good trade-off in terms of capital expenditure and blocking probability. Our proposal provides high quality solutions with a very small execution time when compared to EAs. From our results, our proposal spends less than 1% of the time required by EAs and achieves better results. [ABSTRACT FROM PUBLISHER]

The assessment of optical networks considering physical impairments is frequently accomplished by using time-consuming analysis tools.We propose in this paper to use artificial neural networks to predict the blocking probability of optical networks with dynamic traffic by using topological metrics and general information of the physical layer. The training process is accomplished by supervised learning based on a historical database of networks. We also propose a new and simple topological property to represent the capacity of the network to distribute traffic. From the results, we found that this novel topological property improves the estimator accuracy. We compared the results of our proposal with the outcome of a discrete event simulator for optical networks. The simulator provides an estimate for blocking probability of alloptical networks considering physical impairments. We show that our approach is faster than discrete event simulators; we obtained a speedup of greater than 7500 times, with comparable estimation errors. [ABSTRACT FROM PUBLISHER]

In this letter we extend the analysis of the Power Series Routing (PSR) algorithm, which was previously proposed and analyzed for transparent networks only, to the translucent and opaque networks. The PSR algorithm is a bio-inspired impairment-aware routing algorithm that can be optimized and can adapt itself to the network conditions. We demonstrate that the PSR algorithm is also suitable for these network configurations. We compared the PSR algorithm with other well known routing algorithms and we observed that the PSR algorithm achieved the best performance in all cases. [ABSTRACT FROM PUBLISHER]