Approximation Classes for Real Number Optimization Problems

2006 ◽  
pp. 86-100
Author(s):  
Uffe Flarup ◽  
Klaus Meer
Keyword(s):  
Real Number ◽  
Optimization Problems ◽  
Approximation Classes

scholarly journals COMPLETENESS IN DIFFERENTIAL APPROXIMATION CLASSES

2005 ◽  
Vol 16 (06) ◽  
pp. 1267-1295 ◽  
Author(s):  
GIORGIO AUSIELLO ◽  
CRISTINA BAZGAN ◽  
MARC DEMANGE ◽  
VANGELIS TH. PASCHOS
Keyword(s):  
Approximation Algorithm ◽  
Optimization Problems ◽  
Differential Approximation ◽  
Np Optimization ◽  
Hard Problems ◽  
Approximation Classes

We study completeness in differential approximability classes. In differential approximation, the quality of an approximation algorithm is the measure of both how far is the solution computed from a worst one and how close is it to an optimal one. We define natural reductions preserving approximation and prove completeness results for the class of the NP optimization problems (class NPO), as well as for DAPX, the differential counterpart of APX, and for a natural subclass of DGLO, the differential counterpart of GLO. We also define class 0-APX of the NPO problems that are not differentially approximable within any ratio strictly greater than 0 unless P = NP. This class is very natural for differential approximation, although has no sense for the standard one. Finally, we prove the existence of hard problems for a subclass of DPTAS, the differential counterpart of PTAS.

Real number arithmetic for mixed behavioural and structural descriptions

1990 ◽  
Vol 137 (6) ◽  
pp. 446
Author(s):  
M.G. Hill ◽  
N.E. Peeling ◽  
I.F. Currie ◽  
J.D. Morison ◽  
E.V. Whiting ◽  
...  
Keyword(s):  
Real Number ◽  
Structural Descriptions

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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