A Parallel Granular Sieving Algorithm for Global Optimization

Abstract

Global optimization problems widely exist in the fields of economic model, finance, engineering design and control. Since it is easy to fall into multiple local optimal solutions that are different from the global optimal solution, how to obtain the global optimal solution is a very important subject. Inspired by the recently proposed deterministic global optimization method – Granular Sieving (GrS) algorithm, this paper proposes a parallel method for global optimization – P-GrS. Supported by the mathematical theory of GrS, P-GrS can theoretically guarantee to find the global optimum and the complete set of global optimal solutions through the parallel design of GrS. The method has better performance than the traditional GrS in most bench mark functions, and the results show the feasibility and effectiveness of the algorithm.