New approach for blade roughcast optimal matching based on self adapting genetic algorithm

A new algorithm named self adapting genetic algorithm is presented to avoid the bugs such as the premature problem and the low convergence speed of the standard genetic algorithm. The self adapting mutation operator automatically adjusts the step size of variable so that the convergence speed is improved. Crossover probability and location are calculated by units applicability which enhanced the intelligence of the algorithm. Two special units U0 and U1 ensure the integrality of gene. Duo to the complexity of blade surface of water turbine, the reference mark and coordinate are difficult to establish on the blade roughcast. According to this, the coordinates of the measured data and design surface need transforming to achieve the optimal matching. The self adapting genetic algorithm is applied to solve the optimal matching question and the results suggest that this algorithm has higher operation speed and stability compared with the standard genetic algorithm.