基于模拟退火离散粒子群算法的测试点优化

Optimization for test selection based on simulated annealing binary particle swarm optimization algorithm

针对复杂系统的测试点优化问题,提出一种基于模拟退火离散粒子群(SA-BPSO)算法的测试点优化算法。该算法利用模拟退火算法的概率突跳能力,克服了基本粒子群算法易陷入局部最优解的缺陷。阐述了该算法在系统测试点优化应用中的流程及关键步骤,并且理论分析了该算法的复杂度。仿真结果表明,该算法在计算时间和测试费用方面都优于遗传算法,能够应用于复杂系统的测试点优化。

For the problem of test selection for complex system, a test selection optimization based on Simulated Annealing Binary Panicle Swarm Optimization （SA-BPSO） algorithm was adopted. The probabilistic jumping ability of simulated annealing algorithm was used to overcome the deficiencies of the panicle swarm being easily fall into local optimal solution. The process and key steps of the algorithm for test selection in complex system were introduced, and the complexity of the algorithm was analyzed. The simulation results show that the algorithm has better performance in running time and testing cost compared to genetic algorithm, thus the algorithm can be used to optimize test points of complex system.