Regularity conditions and Farkas-type results for systems with fractional functions

2020 ◽  
Vol 54 (5) ◽  
pp. 1369-1384
Author(s):  
Xiangkai Sun ◽  
Xian-Jun Long ◽  
Liping Tang
Keyword(s):  
Optimization Problems ◽  
Regularity Conditions ◽  
Conjugate Duality ◽  
Conjugate Functions ◽  
Geometrical Constraint ◽  
Convex Constraint ◽  
Dc Optimization ◽  
Special Cases ◽  
Fractional Function

This paper deals with some new versions of Farkas-type results for a system involving cone convex constraint, a geometrical constraint as well as a fractional function. We first introduce some new notions of regularity conditions in terms of the epigraphs of the conjugate functions. By using these regularity conditions, we obtain some new Farkas-type results for this system using an approach based on the theory of conjugate duality for convex or DC optimization problems. Moreover, we also show that some recently obtained results in the literature can be rediscovered as special cases of our main results.

The modified second APG method for DC optimization problems

2018 ◽  
Vol 13 (4) ◽  
pp. 805-824
Author(s):  
Daoling Lin ◽  
Chunguang Liu
Keyword(s):  
Optimization Problems ◽  
Dc Optimization

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.

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