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An M-Objective Penalty Function Algorithm Under Big Penalty Parameters 被引量:1
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作者 ZHENG Ying MENG Zhiqing SHEN Rui 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2016年第2期455-471,共17页
Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization problems.... Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software,which makes them impossible to find out an optimal solution to constrained optimization problems.In this paper,a novel penalty function(called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed.Based on the M-objective penalty function,an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems,with its convergence proved under some conditions.Furthermore,numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters,and is efficient in choosing a penalty parameter in a large range in Matlab software. 展开更多
关键词 ALGORITHM constrained optimization problem m-objective penalty function stability.
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