In this article, we focus to study about modified objective function approach for multiobjective optimization problem with vanishing constraints. An equivalent η-approximated multiobjective optimization problem is co...In this article, we focus to study about modified objective function approach for multiobjective optimization problem with vanishing constraints. An equivalent η-approximated multiobjective optimization problem is constructed by a modification of the objective function in the original considered optimization problem. Furthermore, we discuss saddle point criteria for the aforesaid problem. Moreover, we present some examples to verify the established results.展开更多
The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimiz...The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimization problem.Under some suitable conditions,we establish the equivalence between an optimal solution of interval-valued primal and penalized optimization problem.Moreover,saddle-point type optimality conditions are also established in order to find the relation between an optimal solution of penalized optimization problem and saddle-point of Lagrangian function.Numerical examples are given to illustrate the derived results.展开更多
The purpose of this paper is to devise exact l_(1) exponential penalty function method to solve multiobjective optimization problems with exponentialtype invexity.The conditions governing the equivalence of the(weak)...The purpose of this paper is to devise exact l_(1) exponential penalty function method to solve multiobjective optimization problems with exponentialtype invexity.The conditions governing the equivalence of the(weak)efficient solutions to the vector optimization problem and the(weak)efficient solutions to associated unconstrained exponential penalized multiobjective optimization problem are studied.Examples are given to illustrate the obtained results.展开更多
基金financially supported by the CSIR,New Delhi,India through Grant no.:25(0266)/17/EMR-II
文摘In this article, we focus to study about modified objective function approach for multiobjective optimization problem with vanishing constraints. An equivalent η-approximated multiobjective optimization problem is constructed by a modification of the objective function in the original considered optimization problem. Furthermore, we discuss saddle point criteria for the aforesaid problem. Moreover, we present some examples to verify the established results.
基金the Department of Science and Technology,New Delhi,India(No.SR/FTP/MS-007/2011).
文摘The objective of this paper is to propose an exact l1 penalty method for constrained interval-valued programming problems which transform the constrained problem into an unconstrained interval-valued penalized optimization problem.Under some suitable conditions,we establish the equivalence between an optimal solution of interval-valued primal and penalized optimization problem.Moreover,saddle-point type optimality conditions are also established in order to find the relation between an optimal solution of penalized optimization problem and saddle-point of Lagrangian function.Numerical examples are given to illustrate the derived results.
基金The research of the first author is financially supported by the University Grant Commission,New Delhi,India(No.41-801/2012(SR)).
文摘The purpose of this paper is to devise exact l_(1) exponential penalty function method to solve multiobjective optimization problems with exponentialtype invexity.The conditions governing the equivalence of the(weak)efficient solutions to the vector optimization problem and the(weak)efficient solutions to associated unconstrained exponential penalized multiobjective optimization problem are studied.Examples are given to illustrate the obtained results.
