scholarly journals OPTIMIZATION PROBLEMS WITH DIFFERENCE OF SET-VALUED MAPS UNDER GENERALIZED CONE CONVEXITY

2017 ◽  
Vol 35 (1_2) ◽  
pp. 147-163
Author(s):  
K. DAS ◽  
C. NAHAK
Keyword(s):  
Optimization Problems ◽  
Cone Convexity

Second-Order Composed Radial Derivatives of the Benson Proper Perturbation Map for Parametric Multi-Objective Optimization Problems

2020 ◽  
Vol 37 (04) ◽  
pp. 2040011
Author(s):  
Qilin Wang ◽  
Xiaoyan Zhang
Keyword(s):  
Lipschitz Continuity ◽  
Optimization Problems ◽  
Second Order ◽  
Proper Efficiency ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Benson Proper Efficiency ◽  
Perturbation Map ◽  
Derivatives Of ◽  
Cone Convexity

In this paper, we introduce second-order composed radial derivatives of set-valued maps and establish some of its properties. By applying this second-order derivative, we obtain second-order sensitivity results for parametric multi-objective optimization problems under the Benson proper efficiency without assumptions of cone-convexity and Lipschitz continuity. Some of our results improve and derive the recent corresponding ones in the literature.

scholarly journals Set-valued optimization problems via second-order contingent epiderivative

2020 ◽  
pp. 41-41
Author(s):  
Koushik Das ◽  
Chandal Nahak
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Second Order ◽  
Kkt Conditions ◽  
Contingent Epiderivative ◽  
Convexity Assumptions ◽  
Mixed Types ◽  
Cone Convexity

In this paper, we establish second-order KKT conditions of a set-valued optimization problem and study second-order Mond-Weir, Wolfe, and mixed types duals with the help of second-order contingent epiderivative and second-order generalized cone convexity assumptions.

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.

Large Scale Plant Optimization Problems

1988 ◽  
Author(s):  
Paul Cronin ◽  
Harry Woerde ◽  
Rob Vasbinder
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Plant Optimization
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