Recently, optical orthogonal frequencydivision multiplexing technology has attracted intensive research interest because spectrum-sliced elastic optical networks (EONs) can be constructed based on it. In this paper, we investigate how to serve multicast requests over EONs with multicast-capable routing, modulation level, and spectrum assignment (RMSA). Both EON planning with static multicast traffic and EON provisioning with dynamic traffic are studied. For static EON planning, we formulate two integer linear programming (ILP) models, i.e., the joint ILP and the separate ILP. The joint ILP optimizes all multicast requests together, while the separate ILP optimizes one request each time in a sequential way. We also propose a highly efficient heuristic that is based on an adaptive genetic algorithm (GA) with minimum solution revisits. The simulation results indicate that the ILPs and the GA provide more efficient EON planning than the existing multicast-capable RMSA algorithms that use the shortest path tree (SPT) and the minimal spanning tree (MST). The results also show that the GA obtains more efficient EON planning results than the separate ILP with much less running time, as it can optimize all multicast requests together in a highly efficient manner. For the dynamic EON provisioning, we demonstrate that the GA is also applicable, and it achieves lower request blocking probabilities than the benchmark algorithms using SPT and MST.Index Terms-Adaptive genetic algorithm; Multicast traffic; Optical orthogonal frequency-division multiplexing (O-OFDM); Routing, modulation-level, and spectrum assignment (RMSA).
Using computer vision technology to accurately identify weeds and crops, positioning weed and spraying of weedcide has become a hotspot of precision agriculture. To determine the optimal threshold in image automatic segmentation and solve one-dimensional histogram without obvious peak and valley distribution, image segmentation method based on two-dimensional histogram and Improved Adaptive Genetic Algorithm is proposed. In the method, the genetic algorithm carries on the global optimization to get the threshold rapidly, and the computational method for crossover probability and mutation probability of the Adaptive Genetic Algorithm is improved. The Improved Adaptive Genetic Algorithm can preserve the multifamily of population and the astringency of the algorithm, and can overcome the problems of poor astringency and premature occurrence in Simple Genetic Algorithm. The result shows that the proposed approach greatly enhances the speed of thresholding and has better immunity to Salt and Pepper Noise.
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