A Novel Dynamic Artificial Whale Algorithm Research And Application In Time Series Forecasting

With the gradual recovery of the global economy and the rapid development of the Internet of Things,computer network technology has ushered in rapid progress of each passing day,and the information available for collection for the economy has shown an explosive growth.The surging wave of big data is facing the onset.Therefore,how to use the optimization algorithm to mine useful information about the massive data,and then accurately predict the data to improve industrial efficiency and increase economic returns,which is what many researchers are concerned about and pursue.Time series prediction based on the optimization algorithm is to find the evolution pattern or internal correlation from the time series of the forecasting indicators,and then make a quantitative estimation of the future development trend of the indicators.Accurate and effective forecasting in advance not only helps to rationalize life production plans,effectively solve practical problems such as resource distribution,but also helps managers make correct decisions,formulate sustainable development plans and policies,and improve the economic benefits of the entire society.In recent years,domestic and foreign scholars have conducted a large number of related research methods for hybrid forecasting methods.Although the traditional group intelligence optimization method is simple and easy to implement,it is difficult to accurately characterize the nonlinear characteristics of the data,and satisfactory prediction accuracy cannot be obtained.Based on the artificial neural network prediction method,the learning ability is strong and the calculation speed is fast,which solves the problem that many traditional statistical methods are difficult to handle.However,artificial neural networks are prone to overfitting and the stability of single prediction model is poor,which makes this method has some limitations in time series prediction.In contrast,the combination of different prediction methods formed by the combination of the prediction method,not only combines the advantages of a variety of single prediction methods,but also significantly improves the accuracy of the entire model prediction,its effect is often better than a single prediction model.However,the combination of smart intelligence optimization strategies and predictive models has always been a difficult and challenging problem.As the actual problems tend to be complicated and large-scale,traditional optimization methods based on strict mathematical principles are powerless in implementing predictions.In the framework of artificial intelligence,this paper introduces the whales' local government into the optimization problem and develops a new model of hybrid optimization problems and forecasting models: the artificial whale model and the resulting efficient intelligent optimization algorithm: artificial whale optimization.At the same time,two application examples of artificial whale algorithm in multiobjective optimization and local connection neural network training are given in this paper.Finally,this paper discusses the development patterns of artificial whale behavior model and artificial whale algorithm.The main contributions to this article are as follows:First,this paper gives the principle and detailed description of the artificial whale optimization algorithm,and analyzes the convergence performance of the algorithm and the influence of various parameters on the convergence of the algorithm.Based on the theory,this paper describes the distance for the artificial whale optimization algorithm.Concepts such as neighborhood and vision,and based on the concept of artificial autonomy,the original concept was developed and improved.Then the algorithm was applied into the combinatorial optimization problem.The experimental results proved the effectiveness of the improved algorithm.In addition,the artificial whale algorithm used in this dissertation solves the optimization problem for largescale systems.Second,this paper proposes a hybrid optimization algorithm and a neural networkbased time series prediction model.This paper conbines a hybrid prediction model which can apply parallel computing based on partial connection neural network.This paper presents the theoretical description and algorithm pseudo code of the new hybrid model,and discusses the superiority of the new model.Third,based on the new hybrid strategy,this paper developed four kinds of training algorithms and set up four new hybrid models to study the balance between the predictive model and the fitted model.We apply different training algorithms to partially connect neural networks based on deep learning methods to evaluate the model's value by examining the performance of the model on time series problems.At the same time,we developed MSI indicators to measure the stability of the model.Fourth,the other major contribution of this paper is to explore how the effect of feature learning on partially connected neural networks is reflected in the accuracy of time series prediction and further influence the stability of the model.This article explores the optimal characteristic learning strategies that are applicable to predictive issues based on comparative experiments by introducing the 'pooling' strategy and the 'freeway gate' approach.Combined with the above-mentioned development of artificial whale algorithm,this paper presents a highly efficient feature learning strategy that can be used in hybrid models.Fifth,in particular,in the field of wind energy and financial redeem,the stability of the predictive model plays an important role,because the stability and accuracy of the predictive model in these fields determine both economic benefits and social stability.The empirical results show that the proposed model not only has good prediction accuracy,but also has reliable stability and can be used to solve the forecasting problems in the field of wind energy and financial lending.