Improvement And Application Of Whale Optimization Algorithm

Many problems in natural science and social economy can be described as optimization problems.The research on high-precision algorithm of optimization problems has attracted many researchers.The whale optimization algorithm(WOA)is a new population-based stochastic optimization method,which tends to the global optimal solution by shrinking and spiral updating,and performs well in many fields.But there are still some disadvantages,such as slow convergence speed,low computational accuracy and falling into local optimal solution.Therefore,three improved whale optimization algorithms are proposed in this paper,and they are applied to feature selection,degree reduction of S-? curves and water resources demand forecasting.The research contents are as follows:1.An improved whale optimization algorithm based on adaptive neighborhood and quadratic interpolation strategy(QINWOA)is proposed.The new algorithm designs the average distance from itself to other whales as an adaptive neighborhood radius calculation method,and chooses learning from the optimal solution in the neighborhood to replace the random learning strategy.By using the stationary point of quadratic interpolation function to approach the maximum point of the objective function.It inherits the global search ability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and then improves the convergence speed.A wrapper feature selection method based on binary QINWOA is proposed.Numerical experiments on 23 standard test functions show that QINWOA is better than 9 popular contrast algorithms.12 standard datasets from UCI tested the effectiveness of QINWOA for feature selection.The experimental results show that QINWOA outperforms the other algorithms in terms of improving classification accuracy and reducing feature number.2.A whale optimization algorithm based on skew normal cloud model(SNCWOA)is proposed.Because of the randomness and fuzziness of whale foraging behavior,cloud model is an effective model to describe cognitive uncertainty,which can reflect the randomness and funnizess of concepts,and the skew normal distribution can better describe the behavior of living things when the environment changes,so a new skew normal cloud model is established by combining the skew normal distribution and the skew normal membership function.A whale optimization algorithm based on partial normal cloud model is proposed by using the skew normal cloud model to modify the mechanism of WOA's shrinking and spiral updating,and the adaptive position and skewness parameters in the skew normal cloud are designed to increase the exploration capability in early stage and the exploitation capability in late stage.The experimental results of the latest CEC2017 benchmark functions verify the effectiveness of the proposed algorithm under different strategies and different dimensions,and the degree reduction of S-? curves optimization problem verifies the practicability of the proposed algorithm.3.An improved whale optimization algorithm based on social learning and wavelet mutation strategy(ASLWMWOA)is proposed.The new algorithm designs a new linear incremental probability,which increases the possibility of global search of the algorithm.Based on the social learning principle,the social ranking and social influence are used to construct the social network for the individual,and the adaptive neighborhood learning strategy based on the network relationship is established to achieve the exchange and sharing of information between groups.The Morlet wavelet mutation mechanism is integrated to realize the dynamic adjustment of the mutation space,which enhances the ability of the algorithm to escape from local optimization.The latest CEC2017 benchmark functions confirms the superiority of the proposed algorithm.Water demand forecasting can promote the rational use of water resources and alleviate the pressure on water demand.By analyzing the use of water resources,this paper establishes three models of water demand forecasting,logarithmic model,linear and exponential combination model and linear,exponential and logarithmic hybrid models.In order to accurately estimate the demand for water resources,an improved whale optimization algorithm based on social learning and wavelet mutation strategy is proposed.The water consumption from 2004 to 2016 in Shaanxi Province of China is used for the experiment.The results show that the performance of the proposed algorithm for solving the three water resources forecasting model is better in comparison to other algorithms.The prediction accuracy is as high as 99.68%,which verified the validity of the model and the practicality of the proposed algorithm.