Aiming at the problems of low convergence accuracy of gazelle optimization algorithm and easy to fall into local optimum,an elite inverse learning and Cauchy perturbation-guided improved gazelle optimization algorithm(IGOA)is proposed.Firstly,the gazelle individuals are initialized using an elite reverse learning strategy to improve the quality of the initial solution and increase the population diversity.Secondly,at the beginning of the algorithm iteration,the two-stage nonlinear inertia weights are used to guide the position updating method of the population,which improves the accuracy of the algorithm and balances the global and local searches of the algorithm.Finally,the survival rate-guided Cauchy perturbation strategy is introduced into the position updating formula of the population in the exploration stage to improve the ability of the algorithm to jump out of the local optimum.Experimental tests are carried out on 8 comparison algorithms using 12 benchmark test functions and Wilcoxon rank sum test,and the results show that the improved algorithm has higher optimization accuracy,faster convergence speed and ability to jump out of local optimum.The practicality and effectiveness of IGOA are verified on two real engineering problems,namely,gear train and three-bar truss design.
NETL
NSTL
万方数据
