To solve the problem of premature convergence

this paper presents a method of generating test cases based on orthogonal exploration and particle swarm optimization.First

singular value decomposition is used to estimate the evolution direction and drives the population towards orthogonal direction

so that our method can avoid searching those traversed areas so far and jump out of local optimum.Then

we change the velocity so that it is able to be consistent with the orthogonal direction

and as a result

the population can be affected continually

which can decrease the frequency of singular value decomposition and reduce the time consumption.Finally

the local search is used for the best particle in each generation.The experimental results show that our method has advantages in coverage

running time

and the number of generations.