Abstract: In view of the defects of the hippopotamus optimization algorithm,such as being prone to falling into local optima and having low search accuracy when solving path-planning problems,an improved hippopotamus optimization(IHO) algorithm with multiple strategies is proposed.Firstly,the Latin hypercube sampling method is introduced to initialize the hippopotamus population,which expands the initial exploration scope and enables the algorithm to be more uniformly distributed in the search space.Secondly,a dynamic adaptive convergence factor is introduced to improve the position update method of male hippopotamuses,enhancing the global search ability and reducing the probability of the algorithm falling into local optima.Then,a variable spiral search strategy is introduced in the hippopotamus defense phase to balance the exploitation and exploration abilities of the algorithm and improve the search efficiency.Simulation results of 12 benchmark test functions show that the IHO algorithm has better optimization ability and faster convergence speed compared with the hippopotamus optimization algorithm,grey wolf algorithm,sand cat swarm algorithm,and genetic algorithm.Finally,the IHO algorithm is applied to the path planning of mobile robots.Experimental results show that in 15×15,20×20,and 30×30 grid maps,the IHO algorithm shortens the path by 7.7%,1.8%,and 4.8% respectively compared with the hippopotamus optimization algorithm.It shows significant performance advantages.

Key words: Hippopotamus optimization algorithm, Latin hypercube sampling, Dynamic adaptive convergence factor, Variable spiral search strategy, Path planning