Reactive power optimization is a typical high-dimensional, nonlinear, discontinuous problem. Traditional Genetic algorithm(GA) exists precocious phenomenon and is easy to be trapped in local minima. To overcome this shortcoming, this article will introduce cloud model into Adaptive Genetic Algorithm (AGA), adaptively adjust crossover and mutation probability according to the X-condition cloud generator to use the randomness and stable tendency of droplets in cloud model. The article proposes the cloud adaptive genetic algorithm(CAGA) ,according to the theory, which probability values have both stability and randomness, so, the algorithm have both rapidity and population diversity. Considering minimum network loss as the objective function, make the simulation in standard IEEE 14 node system. The results show that the improved CAGA can achieve a better global optimal solution compared with GA and AGA.
-High Efficiency Video Coding (HEVC) exploits quad-tree structured Coding Unit (CU) to improve compression efficiency. It saves about 50% coding bits as compared with the former standard H.264/AVC high profile. However, the computational complexity is dramatically increased for more partition blocks and coding modes supported in HEVC. In this paper, a fast CU decision algorithm is proposed to reduce the number of candidate CUs for HEVC intra coding, which is consisted of two algorithms: a spatial correlation based early CU decision algorithm (SECU) and a Rate Distortion (RD) cost based early CU decision algorithm (RDCU). The depths of spatially neighboring CUs are exploited to skip unnecessary CU size tests first. Then the distribution of RD cost is utilized on the selection of CU sizes. Experimental results demonstrate that the proposed algorithm can achieve the time saving by 46% on average as compared with original encoder with 0.92% BDBR increase and 0.05 dB BDPSNR decrease.
Index Terms-HEVC, coding unit, intra coding, computational complexityZhilong Zhu received the MD degree from He has attempted many scientific research projects in Artificial Intelligence. His main research areas are in digital electronic technology, computer vision and pattern recognition.Gang Xu received the MD degree from Guilin
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