Some non-smooth optimality results for optimization problems with vanishing constraints via Dini–Hadamard derivative

2022 ◽  
Author(s):  
Gholam Hasan Shirdel ◽  
Maryam Zeinali ◽  
Ali Ansari Ardali
Keyword(s):  
Optimization Problems ◽  
Vanishing Constraints ◽  
Hadamard Derivative

scholarly journals Optimality conditions and Mond–Weir duality for a class of differentiable semi-infinite multiobjective programming problems with vanishing constraints

4OR ◽  
2021 ◽  
Author(s):  
Tadeusz Antczak
Keyword(s):  
Optimality Conditions ◽  
Vector Optimization ◽  
Multiobjective Programming ◽  
Optimization Problems ◽  
Necessary Optimality Conditions ◽  
Sufficient Optimality Conditions ◽  
Smooth Vector ◽  
Duality Results ◽  
Vanishing Constraints ◽  
Multiobjective Programming Problems

AbstractIn this paper, the class of differentiable semi-infinite multiobjective programming problems with vanishing constraints is considered. Both Karush–Kuhn–Tucker necessary optimality conditions and, under appropriate invexity hypotheses, sufficient optimality conditions are proved for such nonconvex smooth vector optimization problems. Further, vector duals in the sense of Mond–Weir are defined for the considered differentiable semi-infinite multiobjective programming problems with vanishing constraints and several duality results are established also under invexity hypotheses.

scholarly journals Semidefinite Multiobjective Mathematical Programming Problems with Vanishing Constraints Using Convexificators

2021 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Kin Keung Lai ◽  
Mohd Hassan ◽  
Sanjeev Kumar Singh ◽  
Jitendra Kumar Maurya ◽  
Shashi Kant Mishra
Keyword(s):  
Optimization Problems ◽  
Sufficient Conditions ◽  
Semidefinite Optimization ◽  
Multiobjective Optimization Problems ◽  
Vanishing Constraints ◽  
Stationary Conditions ◽  
Mathematical Programming Problems ◽  
Multiobjective Mathematical Programming ◽  
Necessary And Sufficient ◽  
Interval Valued

In this paper, we establish Fritz John stationary conditions for nonsmooth, nonlinear, semidefinite, multiobjective programs with vanishing constraints in terms of convexificator and introduce generalized Cottle type and generalized Guignard type constraints qualification to achieve strong S—stationary conditions from Fritz John stationary conditions. Further, we establish strong S—stationary necessary and sufficient conditions, independently from Fritz John conditions. The optimality results for multiobjective semidefinite optimization problem in this paper is related to two recent articles by Treanta in 2021. Treanta in 2021 discussed duality theorems for special class of quasiinvex multiobjective optimization problems for interval-valued components. The study in our article can also be seen and extended for the interval-valued optimization motivated by Treanta (2021). Some examples are provided to validate our established results.

scholarly journals Duality theorems for nondifferentiable semi-infinite interval-valued optimization problems with vanishing constraints

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haijun Wang ◽  
Huihui Wang
Keyword(s):  
Optimization Problem ◽  
Generalized Convexity ◽  
Optimization Problems ◽  
Strong Duality ◽  
Infinite Interval ◽  
Duality Theorems ◽  
Converse Duality ◽  
Vanishing Constraints ◽  
Dual Models ◽  
Interval Valued

AbstractIn this paper, we study the duality theorems of a nondifferentiable semi-infinite interval-valued optimization problem with vanishing constraints (IOPVC). By constructing the Wolfe and Mond–Weir type dual models, we give the weak duality, strong duality, converse duality, restricted converse duality, and strict converse duality theorems between IOPVC and its corresponding dual models under the assumptions of generalized convexity.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Sign in / Sign up

Export Citation Format

Share Document