The Parallel Implementation of Interval Global Optimization Algorithm and Its Application

In this paper, a deterministic global optimization algorithm based on interval analysis is investigated, and a parallel interval computing model based on a branch and deletion strategy is proposed. The new algorithm model is integrated into a Web system. The users can implement applications with B/S structure conveniently, and query the results of applications in the client interface. An application example, namely the safety distance is provided to analyze the feasibility of the algorithm. The parallel optimization algorithm provided in this article can find a safe distance and ensure the reliability of results at the same time. The algorithm can also predict the influence of uncertain parameters on the safety distance more efficient and realistic. Some experiments are given to illustrate the advantage of the new PIBD algorithm.