A New Global Optimization Algorithm for Solving a Class of Nonconvex Programming Problems

Xue-Gang Zhou and Bing-Yuan Cao

Journal of Applied Mathematics, 2014, vol. 2014, 1-10

Abstract:

A new two-part parametric linearization technique is proposed globally to a class of nonconvex programming problems (NPP). Firstly, a two-part parametric linearization method is adopted to construct the underestimator of objective and constraint functions, by utilizing a transformation and a parametric linear upper bounding function (LUBF) and a linear lower bounding function (LLBF) of a natural logarithm function and an exponential function with e as the base, respectively. Then, a sequence of relaxation lower linear programming problems, which are embedded in a branch-and-bound algorithm, are derived in an initial nonconvex programming problem. The proposed algorithm is converged to global optimal solution by means of a subsequent solution to a series of linear programming problems. Finally, some examples are given to illustrate the feasibility of the presented algorithm.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljam:697321

DOI: 10.1155/2014/697321

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