A monotonic optimization approach for solving strictly quasiconvex multiobjective programming problems

被引:6
|
作者
Tran Ngoc Thang [1 ]
Solanki, Vijender Kumar [2 ]
Dao, Tuan Anh [3 ]
Nguyen Thi Ngoc Anh [1 ]
Pham Van Hai [4 ]
机构
[1] Hanoi Univ Sci & Technol, Sch Appl Math & Informat, Hanoi, Vietnam
[2] CMR Inst Technol, Dept Comp Sci & Engn, Hyderabad, TS, India
[3] Uppsala Univ, Dept Informat Technol, Uppsala, Sweden
[4] Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, Hanoi, Vietnam
关键词
Multiobjective programming; monotonic optimization; strictly quasiconvex; outcome space; outer approximation; GLOBAL OPTIMIZATION; POINTS; SET;
D O I
10.3233/JIFS-179690
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article, we use a monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems which include many classes of captivating problems, for example when the criterion functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, an approximation of the weakly efficient solution set is obtained containing the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using convex programming tools. Some computational experiments are reported to show the accuracy and efficiency of the algorithm.
引用
收藏
页码:6053 / 6063
页数:11
相关论文
共 50 条