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.
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