TY - GEN
T1 - Quantum genetic search algorithm based on range image of laser radar
AU - Jianfeng, Sun
AU - Xuefeng, Wang
AU - Tianjiao, Wang
AU - Qi, Wang
PY - 2011
Y1 - 2011
N2 - Quantum evolutionary learning algorithm is a kind of fast algorithm which search the optimal solution of functions, with the help of Quantum thinking, this kind of algorithm has a high degree of parallelism, and the nature of fast speed. In this paper a frame work of quantum genetic search which is based on laser lidar image, in this frame work, a method of image quantum formulation is proposed, a wave function is used to describe the solution space to be searched, which makes the operand transformed from determined points to the whole solution space. Besides, an unequal probability initialization is shown in this paper, which solves the problem of misconvergence in the general initializations. What is more, an adaptive quantum rotary gate is designed to accelerate the convergence of the algorithm, which is adjusted automatically with the evolutional generation and fitness. The frame work given in this paper was applied in the target searching of streak tube imaging lidar, and shows rapid convergence and high stability with the premise of high-precision.
AB - Quantum evolutionary learning algorithm is a kind of fast algorithm which search the optimal solution of functions, with the help of Quantum thinking, this kind of algorithm has a high degree of parallelism, and the nature of fast speed. In this paper a frame work of quantum genetic search which is based on laser lidar image, in this frame work, a method of image quantum formulation is proposed, a wave function is used to describe the solution space to be searched, which makes the operand transformed from determined points to the whole solution space. Besides, an unequal probability initialization is shown in this paper, which solves the problem of misconvergence in the general initializations. What is more, an adaptive quantum rotary gate is designed to accelerate the convergence of the algorithm, which is adjusted automatically with the evolutional generation and fitness. The frame work given in this paper was applied in the target searching of streak tube imaging lidar, and shows rapid convergence and high stability with the premise of high-precision.
KW - imaging liar
KW - quantum genetic algorithm
KW - real-time performance
KW - target search
UR - https://www.scopus.com/pages/publications/84858739843
U2 - 10.1109/AISMOT.2011.6159356
DO - 10.1109/AISMOT.2011.6159356
M3 - 会议稿件
AN - SCOPUS:84858739843
SN - 9781457707964
T3 - 2011 Academic International Symposium on Optoelectronics and Microelectronics Technology, AISOMT 2011
SP - 212
EP - 215
BT - 2011 Academic International Symposium on Optoelectronics and Microelectronics Technology, AISOMT 2011
T2 - 2011 Academic International Symposium on Optoelectronics and Microelectronics Technology, AISOMT 2011
Y2 - 12 October 2011 through 16 October 2011
ER -