Aiming at the problems of low population diversity,low speed of convergence,and the problem that it is prone to fall into local optimum in wild horse optimizer(IWHO),the Tent inertial weight based on hunger games was introduced into the foals position formula to develop the global search and local search ability of the algorithm to acquire a better balance.Refraction mirror learning strategy was used to generate the reverse solution of the feasible solution and improve the precision of the algo-rithm in the grazing phase.An operator was used which was mixed by golden sine and moth-flame,the best position of the wild horse was disturbed to make the algorithm to jump out of local optimum.The improved algorithm(IWHO)was compared with other algorithms on 10 benchmark functions,and Wilcoxon and tension/compression string design problem was used to verify its performance.Simulation results show that IWHO has obvious improvement in convergence speed and optimization accuracy.
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