Improvement And Application Research Of Whale Optimization Algorithm

Whale Optimization Algorithm(WOA)is a new swarm intelligent optimization algorithm proposed based on the hunting behavior of humpback whales.It has the advantages of simple operation,few parameters to adjust,rich comprehensive mechanism and strong ability to jump out of the local optimal.Compared with traditional optimization algorithm,it is more stable and efficient,so it has a wide range of applications in network security prediction,fault diagnosis,feature selection,intrusion detection and other engineering fields.However,the basic whale optimization algorithm has some problems,such as slow convergence speed,low accuracy and easy to fall into local optimum.Therefore,the algorithm is improved from the aspects of whale population initialization,optimization method and mutation strategy,and the improved algorithm is applied to the complex optimization problem with multiple constraints.The purpose is to improve the convergence speed of whale optimization algorithm,improve the theoretical basis of the algorithm and expand the application field.The main research results are as follows:(1)An improved whale optimization algorithm(CGWOA)based on chaotic mapping and Gaussian difference variation is proposed.Firstly,in view of the large random error of the initial population of the original algorithm,Cubic chaotic mapping is adopted to initialize the population,so as to make the population distribution more uniform and improve the quality of the initial solution.Secondly,the nonlinear convergence factor and adaptive inertia weight are introduced in the phase of whale trapping and bubble net hunting to enhance the convergence rate and global development ability of the algorithm.Finally,new individuals are generated by Gaussian difference variation among the populations,which increases the population diversity and makes the algorithm break through the problem of local optimization and expands the search scope of the population in the solution space.Then a flow chart is given to describe the steps of the algorithm in detail and the time and space complexity of the algorithm is analyzed.Experiments were conducted on 10 benchmark test functions,respectively,compared with other optimization algorithms,whale algorithm improved by single strategy,and whale algorithm improved by other literatures.According to the convergence curve of experimental data and Wilcoxon rank sum test,it can be seen that the convergence speed optimization precision robustness of CGWOA has been significantly improved.(2)A hybrid strategy based improved whale optimization algorithm(HSWOA)is proposed.Cauchy variation perturbation is used to improve the convergence rate of the algorithm,and the current optimal individual is guided to jump out of the local optimal.The golden sinusoidal segmentation coefficient is introduced to optimize the whale algorithm,which improves the convergence speed and strengthens the global exploration ability.The Levy-flight strategy was used to expand the search range of whale groups and improve the global search ability.Then the algorithm steps are described in detail and the flow chart is given,and the time complexity of the algorithm is analyzed.The experimental results show that the convergence speed,searching precision and global searching ability of the improved algorithm are significantly improved compared with other optimization algorithms,which proves the effectiveness of the improved algorithm.(3)Vehicle routing problem(VRP)is the core problem to solve logistics distribution and transportation organization optimization.Taking route optimization of logistics distribution as the background objective,CGWOA algorithm is used to solve open vehicle routing problem with capacity constraint.The effectiveness and feasibility of the algorithm in vehicle routing problem are proved by three sets of simulation experiments and comparison with the basic whale optimization algorithm.The HSWOA algorithm is applied to solve the engineering design optimization problems of pressure vessel,welded beam and tensile spring,and the lowest design cost is obtained by experiments.The improved algorithm has certain theoretical value and practical significance.