Abstract: To date,meta-heuristic search algorithms have been applied to automate test data generation.The topology of particle swarm optimization (PSO) is one of the key factors that affect algorithm performance.In order to overcome the phenomena of falling into local optimal and premature convergence of the standard particle swarm algorithm (PSO),a mixture neighborhood structure (MPSO) was proposed to generate software structure test data automatically.Based on the analysis of the different neighborhood topology structure effect on the performance of particle optimization,this paper presented a new particle swarm optimization with mix topological structure.MPSO is based on the combination of global optimization and local optimization.In each generation,by observing the feedback information of diversity of the population,the particle speed update method is selected by global topology model or local topology model.The experimental results show that MPSO increases the swarm diversity,avoids falling into local optimization,and improves the convergence speed of the proposed algorithm.

Key words: Particle swarm algorithm,Automatic test data generation,Topology structure,Global optimization,Local optimization,Diversity