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Simulated annealing method on global optimization of nonconvex NLP problems

Hu Shanying, Chen Bingzhen, He Xiaorong,Shen Jingzhu Department of Chemical Engineering, Tsinghua University, Beijing 100084  
On the basis of simulated annealing(SA) algorithm for nonconstraint nonlinear programming(NLP) problems, an improvement and further approach to global optimization of SA on nonconvex NLP with constraints is introduced. To inequality constraints, a check procedure and a penalty function procedure are proposed, and equality constraints are dealt with by a procedure of solving equations in which tear equations may be converged by an iterating way or a penalty function way. As a result, an SA algorithm for global optimization on nonconvex NLP problems with constraints is developed. Results of examples show that the proposed approach is better than existing methods.
【Fund】: 国家教委留学回国人员科研资助
【CateGory Index】: O221.2
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