基于融合算法的测试优化选择问题研究

测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。

Research on Optimal Test Selection Based on Fused Algorithm

Test optimization selection is a set cover problem,and heuristic algorithm for set covering problem is effective method.A genetic simulated annealing neural network fused algorithm was proposed by fusing the genetic algorithm,BP neural network and the simulated annealing algorithm,the genetic algorithm global search ability,strong ability of BP neural network training algorithm and fast search ability of simulated annealing algorithm were made full use of in this algorithm,the phenomenon falling into local optimum was avoided,and also the search efficiency and accuracy wad improved,the algorithm is applied to solve the test optimization selection problem.Example proves,this algorithm can effectively and quickly obtain test the optimal solution of optimization problems.