A dynamic convexized method for nonconvex mixed integer nonlinear programming
A dynamic convexized method for nonconvex mixed integer nonlinear programming
复制标题
非凸混合整数非线性规划的动态凸化方法
DOI：
10.1016/j.cor.2011.02.014
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发表时间：
2011-12
影响因子：
4.6
通讯作者：
Geng Lin
中科院分区：
文献类型：
--
作者：
Wenxing Zhu;Geng Lin
We consider in this paper the nonconvex mixed-integer nonlinear programming problem. We present a mixed local search method to find a local minimizer of an unconstrained nonconvex mixed-integer nonlinear programming problem. Then an auxiliary function which has the same global minimizers and the same global minimal value as the original problem is constructed. Minimization of the auxiliary function using our local search method can escape successfully from previously converged local minimizers by taking increasing values of parameters. For the constrained nonconvex mixed-integer nonlinear programming problem, we develop a penalty based method to convert the problem into an unconstrained one, and then use the above method to solve the later problem. Numerical experiments and comparisons on a set of MINLP benchmark problems show the effectiveness of the proposed algorithm.
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