Abstract: In order to solve the problem of premature convergence and being easy to fall into local extremum in standard particle swarm optimization,this paper put forward a particle swarm optimization based on reverse-learning and search-again for test case generation.Firstly,the learning factor is improved by the nonlinear decreasing inertia weight function,realizing the preliminary search for the population,and the gradient descent method is used to complete the search-again of the optimal solution and the suboptimal solution.Secondly,setting taboo areas with extreme points as the center,the population diversity is improved by the reverse learning of the particles outside the taboo region.Finally,the branch distance method is used to construct fitness function to evaluate the quality of test cases.Experiment results show that the proposed method has advantages in coverage,iteration times and defect detection rate.

Key words: Learning factors, Particle swarm optimization, Reverse learning, Search again, Test case generation