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Two modified Pascoletti–Serafini methods for solving multiobjective optimization problems and multiplicative programming problems.
- Source :
-
Soft Computing - A Fusion of Foundations, Methodologies & Applications . Nov2023, Vol. 27 Issue 21, p15675-15697. 23p. - Publication Year :
- 2023
-
Abstract
- In this paper, a modified Pascoletti–Serafini scalarization approach, called MOP_MPS, is proposed to generate approximations of a Pareto front of bounded multi-objective optimization problems (MOPs). The objective is obtaining some points with an almost even distribution overall Pareto front. This algorithm is applied to six test problems with convex, non-convex, connected, and dis-connected Pareto fronts, and its results are compared with results of some famous algorithms. The results emphasize that MOP_MPS is effective and competitive in comparing with the other considered algorithms. In addition, it is shown that an optimal solution of a multiplicative programming problem is a properly Pareto optimal solution of an MOP. By considering this relation between MOPs and multiplicative programming problems (MPPs), another algorithm based on MOP_MPS, called MPP_MPS, is suggested for approximately solving non-linear MPPs in which functions multiplied are continuous and bounded from below. The computational results on seven problems of convex MPPs demonstrate that the algorithm is much better than a cut and bound algorithm presented by Shao and Ehrgott in terms of CPU time. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PARETO optimum
*PARETO distribution
Subjects
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 27
- Issue :
- 21
- Database :
- Academic Search Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 171991494
- Full Text :
- https://doi.org/10.1007/s00500-023-08809-2