Research On Improved Whale Optimization Algorithm Based On Mixed Strategy

The swarm intelligence optimization algorithm is a computer model that simulates the collective behavior of social animals in nature.The whale optimization algorithm model is a new type of intelligent optimization and improvement algorithm that is quite popular in recent years.It is one of the meta-heuristic algorithms and has obvious first-mover advantages: simple coordination mechanism,few comprehensive reference factors,simple structure,and strong application.Because of this,the whale optimization algorithm has relatively obvious advantages compared with other algorithms,and it has been successfully applied in many directions,and it is one of the most interesting algorithms in the recent computer field.Since its launch in 2016,the algorithm has received extensive attention from scholars from all walks of life at home and abroad.In the past 6 years,the whale optimization algorithm has been successfully applied to various industries such as workshop scheduling,function optimization,image recognition,and medical imaging.Even so,the research on the algorithm is still in the stage of improvement,and there are still some basic problems,such as: easy to fall into local optimum,low convergence accuracy,unbalanced global and local search,etc.,which undoubtedly affect the development and application of the algorithm.In this paper,according to the defects of the whale optimization algorithm,the detailed research and improvement are carried out,and the performance of the whale optimization is improved by a mixed strategy,that is,multiple strategies are improved separately.In addition,the feature selection and discrete optimization problems of the whale optimization algorithm(0-1 Backpack)for practical applications.All in all,it is to discuss the whale optimization algorithm from its theory and application.The main work is as follows:(1)A whale optimization algorithm based on hybrid strategy to improve(TQAWOA),in view of the original population the problem of large random initialization randomicity,using chaos Tent map instead of the original population,make its produce good quality chaos initial population diversity,the strategy to ensure diversity of population and produce excellent whale populations,provide the conditions for global search;To solve the problem of slow convergence,the adaptive weight strategy was added in the contraction and enveloping stages to balance global search and local optimization.In order to improve the accuracy of population calculation,a new whale individual was generated by quadratic interpolation strategy and the local optimal solution was updated by greedy strategy.The results show that the improved whale optimization algorithm(TQAWOA)achieves ideal optimization effect by comparing different intelligent optimization algorithms on 15 benchmark functions.(2)A mixed strategy improved whale optimization algorithm in solving 0-1knapsack,the application of the improved optimization algorithm(TQAWOA)whale optimization mechanism,on the basis of introducing the levy flight mechanism,to improve the searching efficiency of algorithm,to prevent the population into local optimal value lead to population aggregation,improve the ability of the optimization of the algorithm.The improved algorithm TQALWOA was tested on six 0-1 knapsacks with different capacities.The experimental data show that the improved algorithm has excellent effect and is feasible in the application of 0-1 knapsacks,which expands the solution idea of intelligent optimization algorithm in the field of discrete optimization and broadens the application range of the algorithm...