An improved whale optimization algorithm based on multiple strategies
To address the issues of the standard whale optimization algorithm,including slow con-vergence speed,imbalance between exploration and exploitation,lack of information exchange among the population,and susceptibility to local optima,an improved algorithm is proposed.Firstly,the Tent chaotic mapping is employed to enhance the uniformity of the initial population distribution.Secondly,a nonlinear convergence factor is introduced to improve the algorithm's global search ability in the early stage and local exploration ability in the middle and late stages,coordinating the transition mechanism between search and exploitation.Then,the average position vector of the population is introduced into the random search process,effectively addressing the lack of information exchange between individuals and the population.Next,an adaptive inertia weight is introduced into the position update formula to enhance the convergence speed and accuracy of the algorithm.Finally,the Cauchy operator is utilized to perform mutation perturbation on individuals trapped in local optima.Simulation experiments were con-ducted on 15 benchmark test functions to evaluate the improved algorithm.The experimental results demonstrate that the improved whale optimization algorithm possesses excellent performance,and the effectiveness of the improved algorithm is proven through the Wilcoxon rank-sum test
万方数据
维普
