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We address the design of fast wavelength assignment and reconfiguration using cognitive management and control that can quickly and accurately adapt to the operating conditions for optical networks. The traffic detection performances of two Bayesian estimators and a stopping-trial (sequential) estimator are examined based on the transient behaviors of networks. The stopping-trial estimator has the fastest response time to the changes of traffic arrival statistics. We propose a wavelength reconfiguration algorithm with continuous assessment where the system reconfigures whenever it deems necessary. The reconfiguration can involve addition or subtraction of multiple wavelengths. Using the fastest detection and reconfiguration algorithm can reduce queueing delays during traffic surges without over provisioning and thus can reduce network capital expenditure and prevent waste of resources upon erroneous decision on occurrence of surges.