A Statistical Process Control Approach to Global Optimization of System Integration

Abstract

Although the traditional control algorithm produces many successful results on system integration applications, there still exist insufficiencies need to be improved. For traditional control approach, it is well known that the search equation is superior at local range but constrained at global range for system integration, which will transform the optimal performance of the algorithm. Therefore, the search ability is the most important goal in algorithm modification on system integration. To solve the faults in traditional control algorithm and achieve the goal of improvement, a new statistical process control approach is proposed to global optimization of system integration. In order to balance the search ability, search algorithms are made better by search strategies that search probability and random variable is related with a certain degree of correlation. In addition, to deepen the convergence degree, the local search operator is also utilized. The simulation results tested show that the proposed process control approach can outperform control approach in most of the experiments.