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Fault management has long been an indispensable component for controlling and managing telecommunication networks. To prevent huge data losses, it is necessary to develop a fast and efficient fault localization mechanism. In this work, we study the problem of multi-failure localization in transparent optical networks. In this context, a correlation-based approach is introduced to exploit the quality of transmission of acquired lightpaths to localize the faulty links. The proposed search-based framework can be implemented by leveraging any search algorithm. One may utilize the exhaustive search method to localize faulty links more accurately, but at the cost of taking more time. On the other hand, one may utilize intelligent search methods with the aim of reducing the required time for localization at the expense of accuracy. However, we propose to use both of the search approaches together. In this way, faulty links are first localized by the intelligent search methods to reroute and restore the failed traffic as fast as possible to prevent further loss of data. To this aim, a genetic algorithm (GA) is proposed to search among the suspected links. Subsequently, exhaustive search method can be utilized to localize failures more accurately without time constraint and then send technicians to the right site to recover the faulty links. The obtained results reveal that the proposed GA approach achieves overall high localization accuracy (98.6%–100%) that is insignificantly affected as the traffic load decreases. Dual and triple-failure incidents are localized within 42–80 ms and 596–2180 ms, respectively. It is shown that the mean time required for localizing failures using the GA search algorithm is significantly lower than exhaustive search approach by several orders of magnitude. Hence, the proposed GA-based fault localization algorithm can reduce the average time required to restore the traffic in case of failures, applicable for the restoration applications. [ABSTRACT FROM AUTHOR]

In this work, we present a co-evolutionary genetic (CEGA) algorithm to adapt the optical launch powers and optimize the signal-to-noise ratio (SNR) values based on maximizing the minimum SNR margin. The introduced co-evolutionary algorithm provides lower computational complexity rather than convex optimization and linear programming techniques, applicable for both static and time-critical dynamic networking. The enhanced Gaussian noise nonlinear model is exploited to take the physical-layer impairments into account, considering networks with partial spectrum utilization. To optimize the minimum SNR margin, we formulate the power allocation problem as a minimax optimization problem. To this end, a two-space genetic algorithm (GA) is proposed to reduce the computational complexity. The obtained results demonstrate that the introduced co-evolutionary algorithm outperforms the common optimization methods in terms of run time. It is shown that the computational complexity of proposed co-evolutionary algorithm is significantly lower than convex and single-space evolutionary approaches by several orders of magnitude. Moreover, the minimum SNR margin is improved by about 2.4 dB compared to a flat launch power optimization. • A CEGA algorithm is proposed to adapt the optical launch powers, applicable for both static and dynamic optical networking. • The EGN model is exploited to consider the physical-layer impairments in networks with partial spectrum utilization. • It is shown that the computational complexity of proposed algorithm is significantly lower than previous approaches. [ABSTRACT FROM AUTHOR]