Test Optimization Selection Method Based on Particle Swarm Optimization-differential Evolution Algorithm
Test optimization selection is a key step in equipment testability design.To avoid falling into lo-cal optima,the particle swarm optimization-differential evolutionary algorithm(PSO-DE)optimization method with added information exchange mechanism is proposed.After establishing a multi-dimensional spatial testability model by fusing multi-signal flow graph with Bayesian network,the PSO DE algorithm is used to achieve a fast and accurate solution.The analysis of the power supply system shows that the method satisfies the testability design requirements,and the searched test set makes the system optimal in terms of testability indexes and the fastest convergence rate.Compared with other optimization algo-rithms,it has the advantages of fast convergence to the global optimum,thus verifying the feasibility of the method.
万方数据
维普
