Adaptive Genetic Annealing Algorithm Research Articles

Multi objective reactive power and voltage optimization of distribution network based on adaptive genetic annealing algorithm

In this paper, a multi-objective reactive power optimization model of distribution network is established, which takes the minimum of line loss, reactive compensation capacitor switching loss and node voltage deviation as objective, considering the constraints of node voltage, reactive power of wind turbine, capacitor switching times. The improved adaptive genetic annealing algorithm is used to solve the model. IEEE33 system is taken as an example to verify the effectiveness of the reactive power optimization model. When the shunt capacitor and wind turbine are in the optimal reactive power compensation, the line loss of the distribution network can be minimized.

Nonlinear camera model calibrated by neural network and adaptive genetic-annealing algorithm

In order to calibrate camera with radial distortion model, a novel approach based on the hybrid neural network with rotational weight matrix and self-adaptive genetic-annealing algorithm is proposed. Firstly two sorts of neural networks are structured, whose weights are corresponding to the camera's extrinsic parameters and intrinsic parameters without and with radial distortion, so the structured neural networks coincide with the camera's pin hole model and radial distortion model respectively. And the performance index is obtained from the square of 2-norm of the difference between the vector consisted of network's outputs and the tested retinal coordinates of corresponding feature points projected in image planes. At the same time, a genetic-annealing algorithm is introduced into the solving-programming, where the probabilities of crossover and mutation are tuned according to the distance density of individuals, and unequal probability matching strategy is adopted. Thus while the system come to the equilibrium, the camera's parameters with radial distortion are obtained in the light of network's weights. The experimental results illustrate that the proposed approach is robust, and has the advantages over existing algorithms in calibration precision, and orthogonality of rotational matrix, in particular the precision of intrinsic and extrinsic parameters of camera, which provides a practical scheme for calibrating camera with radial distortion model.