Research And Application Of Intelligent Algorithm

As a new kind of swarm intelligence algorithm,artificial bee colony algorithm is followed with interest since it was put forward.It has a simple structure and few parameters,so it is easy to implement.At the same time it has certain advantages in the global search ability,so it is widely used for solving the optimal value function and artificial neural network etc.However,there are some shortcomings of the algorithm,the traditional artificial bee colony algorithm is not optimistic about the local search ability,it has a low convergence accuracy and is easy to fall into premature convergence etc.Considering the effect of the algorithm,this paper makes the algorithm better,and proposes an artificial bee colony algorithm based on quantum behaved Particle Swarm Optimization(PSO).In this algorithm,the particle swarm optimization algorithm is introduced into the local search strategy of the following bees,which makes the artificial bee colony have higher local search ability.The simulation results of 10 standard test functions show that compared with the traditional artificial bee colony algorithm,the improved artificial bee colony algorithm has greatly improved the convergence speed and optimization accuracy.In this paper,the traditional prediction methods and the new prediction methods are analyzed.Because the support vector machine has good prediction ability,the support vector machine is used to predict the electric energy.However,there are some defects in the support vector machine,and the choice of penalty factor and kernel function parameter affects the prediction results to a certain extent.In this paper,the improved artificial bee colony algorithm is used to optimize the parameters of support vector machine.The experimental results show that compared with other similar algorithms,the proposed algorithm has a significant improvement in prediction accuracy and accuracy.Short term load forecasting is of great significance in life.It not only ensures the security and stability of the power grid,but also is one of the important tasks of the power system.Accurate load forecasting can reduce the redundant backup capacity of enterprises,and improve the efficiency of power grid,so it can ensure the stability of the power grid;water quality prediction determines the amount of aquaculture,water quality prediction can improve the safety of aquaculture products,and ensure the balance of the ecosystem.In this paper,a predictive model based on improved artificial bee colony algorithm is established,and the model is applied to the field of electric power and water quality prediction.In the process of prediction,we need to abnorm data processing and normalizate the historical data,after the improvement of the training samples,the prediction model of training sample is setting for training,after training we can get test results.The experimental results show that the improved model has better prediction accuracy than other models.