A priority measure in interval methods for constrained global optimization

The interval method is a common approach for global optimization. Some interval methods are based on the branch and bound principle. For constrained global problems the authors find that the order of branching plays a very important role in determining the efficiency of the method. In this paper a priority criterion for subdivision is proposed that combines the objective function value with a feasibility measure. The resulting algorithm is convergent. After a series of tests, the present experiment results show that the new priority order can impove the performance of the previous method remarkably. In particular, for 60 randomly generated test problems, on average more than 80% of branchings and 70% of storage units can be saved. Some empirical formulae for deciding the parameters of the new algorithm are also suggested.